DSCC_Net: multi-classification deep learning models for diagnosing of skin cancer using dermoscopic images
Open accessAbstract
Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Data augmentation is typically treated as supplementary rather than integral.
“Data augmentation is typically treated as supplementary rather than integral (11, 12).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [11]
The Materials and Methods section (3.3; page unavailable) describes using SMOTE Tomek to balance this study’s dataset, and the Results section (4.9; page unavailable) discusses its effect. Neither passage addresses whether data augmentation is typically treated as supplementary rather than integral; searches for that phrasing and for augmentation as an additional training step found no such discussion. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
Han, H., Wang, W.-Y., & Mao, B.-H. (2005) · Lecture notes in computer science · doi:10.1007/11538059_91 · Cited by 4,331 · see citing works
Tschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628 · see citing works
Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the US Population, 2012
Rogers, H. W., Weinstock, M. A., Feldman, S. R., & Coldiron, B. M. (2015) · JAMA Dermatology · doi:10.1001/jamadermatol.2015.1187 · Cited by 1,815 · see citing works
Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A. B. H., Thomas, L., Enk, A., et al. (2018) · Annals of Oncology · doi:10.1093/annonc/mdy166 · Cited by 1,571 · see citing works
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.12.003 · Cited by 895 · see citing works
A methodological approach to the classification of dermoscopy images
Celebi, M. E., Kingravi, H. A., Uddin, B., Iyatomi, H., Aslandogan, Y. A., Stoecker, W. V., & Moss, R. H. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.01.003 · Cited by 697 · see citing works
Ali, M. S., Miah, M. S., Haque, J., Rahman, M. M., & Islam, M. K. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100036 · Cited by 415 · see citing works
Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images
Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A. C., & Smith, J. R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24888-2_15 · Cited by 400 · see citing works
Pathological brain detection based on AlexNet and transfer learning
Lu, S., Lu, Z., & Zhang, Y. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.11.008 · Cited by 385 · see citing works
Ali, K., Shaikh, Z. A., Khan, A. A., & Laghari, A. A. (2021) · Neuroscience Informatics · doi:10.1016/j.neuri.2021.100034 · Cited by 371 · see citing works
Deep features to classify skin lesions
Kawahara, J., BenTaieb, A., & Hamarneh, G. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493528 · Cited by 356 · see citing works
Skin Lesion Classification from Dermoscopic Images Using Deep Learning Techniques
Romero-López, A., Giró-i-Nieto, X., Burdick, J., & Marques, O. (2017) · Biomedical Engineering · doi:10.2316/p.2017.852-053 · Cited by 355 · see citing works
A survey on deep learning in medicine: Why, how and when?
Piccialli, F., Somma, V. D., Giampaolo, F., Cuomo, S., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.09.006 · Cited by 352 · see citing works
Iqbal, I., Younus, M., Walayat, K., Kakar, M. U., & Ma, J. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101843 · Cited by 343 · see citing works
Overview of Advanced Computer Vision Systems for Skin Lesions Characterization
Maglogiannis, I. G., & Doukas, C. N. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2017529 · Cited by 342 · see citing works
Hybrid convolutional neural networks with SVM classifier for classification of skin cancer
Keerthana, D., Venugopal, V., Nath, M. K., & Mishra, M. (2022) · Biomedical Engineering Advances · doi:10.1016/j.bea.2022.100069 · Cited by 280 · see citing works
Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., & R. Oleiwi, S. (2020) · Applied Sciences · doi:10.3390/app10134523 · Cited by 258 · see citing works
Saba, T., Khan, M. A., Rehman, A. U., & Larabi-Marie-Sainte, S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1413-3 · Cited by 250 · see citing works
Khan, M. A., Akram, T., Zhang, Y., & Sharif, M. I. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.12.015 · Cited by 247 · see citing works
Deep Learning Solutions for Skin Cancer Detection and Diagnosis
Nahata, H., & SINGH, S. P. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-40850-3_8 · Cited by 222 · see citing works
An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset
Alam, T. M., Shaukat, K., Khan, W. A., Hameed, I. A., Almuqren, L., Raza, M. A., Aslam, M., & Luo, S. (2022) · Diagnostics · doi:10.3390/diagnostics12092115 · Cited by 207 · see citing works
The melanoma skin cancer detection and classification using support vector machine
Alquran, H., Abu-Qasmieh, I. F., Alqudah, A. M., Al-Hammouri, S., Alawneh, E., Abughazaleh, A., & Hasayen, F. (2017) · IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies · doi:10.1109/aeect.2017.8257738 · Cited by 204 · see citing works
Naeem, A., Farooq, M. S., Khelifi, A., & Abid, A. (2020) · IEEE Access · doi:10.1109/access.2020.3001507 · Cited by 189 · see citing works
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
Kaur, R., GholamHosseini, H., Sinha, R., & Lindén, M. (2022) · Sensors · doi:10.3390/s22031134 · Cited by 186 · see citing works
Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks
Bajwa, M. N., Muta, K., Malik, M. I., Siddiqui, S. A., Braun, S. A., Homey, B., Dengel, A. R., & Ahmed, S. (2020) · Applied Sciences · doi:10.3390/app10072488 · Cited by 183 · see citing works
Automatic detection of blue-white veil and related structures in dermoscopy images
Celebi, M. E., Iyatomi, H., Stoecker, W. V., Moss, R. H., Rabinovitz, H. S., Argenziano, G., & Soyer, H. P. (2008) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2008.08.003 · Cited by 173 · see citing works
Bleeding Frame and Region Detection in the Wireless Capsule Endoscopy Video
Yuan, Y., Li, B., & Meng, M. Q. ‐. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2399502 · Cited by 167 · see citing works
Skin cancer disease images classification using deep learning solutions
Mijwil, M. M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10952-7 · Cited by 154 · see citing works
Melanoma detection using deep learning-based classifications
Alwakid, G., Gouda, W., Humayun, M., & Sama, N. U. (2022) · Healthcare · doi:10.3390/healthcare10122481 · Cited by 153 · see citing works
Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application
Kousis, I., Perikos, I., Hatzilygeroudis, I., & Virvou, M. K. (2022) · Electronics · doi:10.3390/electronics11091294 · Cited by 148 · see citing works
Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma
Carrera, C., Marchetti, M. A., Dusza, S. W., Argenziano, G., Braun, R. P., Halpern, A. C., Jaimes, N., Kittler, H., Malvehy, J., Menzies, S. W., et al. (2016) · JAMA Dermatology · doi:10.1001/jamadermatol.2016.0624 · Cited by 143 · see citing works
Skin Cancer Detection Using Convolutional Neural Network
Hasan, M., Barman, S. D., Islam, S., & Reza, A. W. (2019) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3330482.3330525 · Cited by 143 · see citing works
Computer-aided Diagnosis of Skin Cancer: A Review
Razmjooy, N., Ashourian, M., Karimifard, M., Estrela, V. V., Loschi, H., Nascimento, D., França, R. P., & Vishnevskii, M. P. (2020) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405616666200129095242 · Cited by 143 · see citing works
Malik, H., Anees, T., din, M.-Z., & Naeem, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13843-7 · Cited by 141 · see citing works
Magnetically guided capsule endoscopy
Shamsudhin, N., Zverev, V. I., Keller, H., Pané, S., Egolf, P. W., Nelson, B. J., & Tishin, A. M. (2017) · Medical Physics · doi:10.1002/mp.12299 · Cited by 140 · see citing works
A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
Khanna, V. V., Chadaga, K., Sampathila, N., Prabhu, S., Bhandage, V. A., & Hegde, G. K. (2023) · Applied System Innovation · doi:10.3390/asi6020032 · Cited by 138 · see citing works
Carli, P., Quercioli, E., Sestini, S., Stante, M., Ricci, L., Brunasso, A. M. G., & GIORGI, V. D. (2003) · British Journal of Dermatology · doi:10.1046/j.1365-2133.2003.05023.x · Cited by 132 · see citing works
Naeem, A., Anees, T., Fiza, M., Naqvi, R. A., & Lee, S. W. (2022) · Sensors · doi:10.3390/s22155652 · Cited by 128 · see citing works
Skin Cancer Disease Detection Using Transfer Learning Technique
Rashid, J., Ishfaq, M., Ali, G., Saeed, M. R., Hussain, M., Alkhalifah, T., Alturise, F., & Samand, N. (2022) · Applied Sciences · doi:10.3390/app12115714 · Cited by 126 · see citing works
Polat, K., & Koç, K. O. (2020) · Journal of Artificial Intelligence and Systems · doi:10.33969/ais.2020.21006 · Cited by 124 · see citing works
Sampathila, N., Chadaga, K., Goswami, N. G., Chadaga, R., Pandya, M. A., Prabhu, S., Bairy, G. M., Swathi, K. S., Bhat, D., & Upadya, P. S. (2022) · Healthcare · doi:10.3390/healthcare10101812 · Cited by 121 · see citing works
Skin Cancer Classification Using Deep Spiking Neural Network
Gilani, S. Q., Syed, T., Umair, M., & Marques, O. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00776-2 · Cited by 121 · see citing works
A hierarchical three-step superpixels and deep learning framework for skin lesion classification
Afza, F., Sharif, M., Mittal, M., Khan, M. A., & Hemanth, D. J. (2021) · Methods · doi:10.1016/j.ymeth.2021.02.013 · Cited by 118 · see citing works
Dermoscopy of Melanoma and Non-melanoma Skin Cancers
Kato, J., Horimoto, K., Sato, S., Minowa, T., & Uhara, H. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00180 · Cited by 116 · see citing works
A Novel Probability Binary Particle Swarm Optimization Algorithm and Its Application
Wang, L., Wang, X., Fu, J., & Zhen, L. (2008) · Journal of Software · doi:10.4304/jsw.3.9.28-35 · Cited by 105 · see citing works
Malik, H., Farooq, M. S., Khelifi, A., Abid, A., Nasir Qureshi, J., & Hussain, M. (2020) · IEEE Access · doi:10.1109/access.2020.3004766 · Cited by 104 · see citing works
Naeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks
Aljohani, K., & Turki, T. (2022) · AI · doi:10.3390/ai3020029 · Cited by 99 · see citing works
Acharya, V., Dhiman, G., Prakash, K., Bahadur, P., Choraria, A., Sushobhitha, M., Sowjanya, J., Prabhu, S., Chadaga, K., Viriyasitavat, W., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2399428 · Cited by 93 · see citing works
Yogapriya, J., Venkatesan, C., Sumithra, M. G., Anitha, P., Jenopaul, P., & Dhas, C. S. G. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5940433 · Cited by 92 · see citing works
Skin Lesions Classification Using Deep Learning Based on Dilated ConvolutionPreprint
Ratul, M. A. R., Mozaffari, M. H., Lee, W., & Parimbelli, E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/860700 · Cited by 88 · see citing works
Nugroho, A. A., Slamet, İ., & Sugiyanto, S. (2019) · AIP conference proceedings · doi:10.1063/1.5141652 · Cited by 81 · see citing works
Wang, S., Xing, Y., Zhang, L., Gao, H., & Zhang, H. (2019) · Computational and Mathematical Methods in Medicine · doi:10.1155/2019/7546215 · Cited by 72 · see citing works
Classification of Malaria Using Object Detection Models
Krishnadas, P., Chadaga, K., Sampathila, N., Rao, S., Swathi, K. S., & Prabhu, S. (2022) · Informatics · doi:10.3390/informatics9040076 · Cited by 66 · see citing works
Mohapatra, S., Nayak, J., Mishra, M., Pati, G. K., Naik, B., & Swarnkar, T. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00417-8 · Cited by 63 · see citing works
Using single-cell technologies to map the human immune system — implications for nephrology
Stewart, B. J., Ferdinand, J. R., & Clatworthy, M. R. (2019) · Nature Reviews Nephrology · doi:10.1038/s41581-019-0227-3 · Cited by 60 · see citing works
Transfer Learning Based Method for Two-Step Skin Cancer Images Classification
Moldovan, D. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8970067 · Cited by 51 · see citing works
Zhou, Y., Koyuncu, C., Lu, C. Y., Grobholz, R., Katz, I. A., Madabhushi, A., & Janowczyk, A. R. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102702 · Cited by 40 · see citing works
Indoor tanning and skin cancer in Canada: A meta-analysis and attributable burden estimation
O'Sullivan, D. E., Brenner, D. R., Demers, P. A., Villeneuve, P. J., Friedenreich, C. M., & King, W. D. (2019) · Cancer Epidemiology · doi:10.1016/j.canep.2019.01.004 · Cited by 39 · see citing works
A Technical Review Report on Deep Learning Approach for Skin Cancer Detection and Segmentation
Keerthana, D., & Nath, M. K. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-8335-3_9 · Cited by 37 · see citing works
Gabor capsule network with preprocessing blocks for the recognition of complex images
Ayidzoe, M. A., Yu, Y., Mensah, P. K., Cai, J., Adu, K., & Tang, Y. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01221-6 · Cited by 37 · see citing works
Wen, F., & David, A. K. (2001) · Artificial Intelligence in Engineering · doi:10.1016/s0954-1810(01)00002-4 · Cited by 35 · see citing works
Chadaga, K., Prabhu, S., Sampathila, N., Chadaga, R., Swathi, K. S., & Sengupta, S. (2022) · Cogent Engineering · doi:10.1080/23311916.2022.2143040 · Cited by 35 · see citing works
Naeem, A., Anees, T., Ahmed, K. T., Naqvi, R. A., Ahmad, S., & Whangbo, T. K. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00866-8 · Cited by 34 · see citing works
Classification of Skin Lesions in Dermatoscopic Images with Deep Convolution Network
Çevik, E., & Zengin, K. (2019) · European Journal of Science and Technology · doi:10.31590/ejosat.638247 · Cited by 28 · see citing works
Skin cancer segmentation and classification with improved deep convolutional neural network
Alom, M. Z., Aspiras, T. H., Taha, T. M., & Asari, V. K. (2020) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2550146 · Cited by 21 · see citing works
Biopsy guided by dermoscopy in cutaneous pigmented lesion - Case report
Bomm, L., Benez, M., Pineiro‐Maceira, J. M., Succi, I. C. B., & Alves, M. D. F. G. S. (2013) · Anais Brasileiros de Dermatologia · doi:10.1590/s0365-05962013000100020 · Cited by 20 · see citing works
Ulcer Detection in Wireless Capsule Endoscopy Images Using Bidimensional Nonlinear Analysis
Charisis, V. S., Tsiligiri, A., Hadjileontiadis, L. J., Liatsos, C. N., Mavrogiannis, C. C., & Sergiadis, G. D. (2010) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-13039-7_59 · Cited by 17 · see citing works
Nordmann, N. J., Hubbard, M. E., Nordmann, T., Sperduto, P. W., Clark, H. B., & Hunt, M. A. (2017) · Cureus · doi:10.7759/cureus.1943 · Cited by 13 · see citing works
Dermoscopy versus skin biopsy in diagnosis of suspicious skin lesions
Ibrahim, H. M., Taieb, M. A. E., Hamed, A., Hamada, R., & Nada, E. (2017) · Al-Azhar Assiut Medical Journal · doi:10.4103/azmj.azmj_67_17 · Cited by 13 · see citing works
Skin cancer: Types, diagnosis and prevention
(2013) · Clinical pharmacist · doi:10.1211/cp.2013.11129534 · Cited by 4 · see citing works
Intelligent skin cancer diagnosis using adaptive k‐means segmentation and deep learning models
Enturi, B. K. M., Suhasini, A., & Satyala, N. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7546 · Cited by 4 · see citing works
The Somatic Genetics of Human Melanoma
Hodis, E. (2018) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 3 · see citing works
Flagged first, then by citations
2 of the 76 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [44]Le Duyen N.T. Hieu X.L. Lua T.N. Hoan T.N. Transfer learning with class-weighted and focal loss function for automatic skin cancer classification arXiv 2020 2009.05977
- [45]Saksham B. Gomekar A. Deep learning diagnosis of pigmented skin lesions Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Kanpur, India 6–8 July 2019 1 6
- [48]Tomáš M. Bajić B. Yildirim S. Hardeberg J.Y. Lindblad J. Sladoje N. Ensemble of convolutional neural networks for dermoscopic images classification arXiv 2018 1808.05071
Cited by
Sorted by citations
A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images
Venugopal, V., Raj, N. I., Nath, M. K., & Stephen, N. S. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100278 · Cited by 135 · see citing works
Himel, G. M. S., Islam, M. M., Al-Aff, K. A., Karim, S. I., & Sikder, M. K. U. (2024) · International Journal of Biomedical Imaging · doi:10.1155/2024/3022192 · Cited by 118 · see citing works
Naeem, A., Anees, T., Khalil, M., Zahra, K., Naqvi, R. A., & Lee, S. W. (2024) · Mathematics · doi:10.3390/math12071030 · Cited by 103 · see citing works
Bibi, S., Khan, M. A., Shah, J. H., Damaševičius, R., Alasiry, A., Marzougui, M., Alhaisoni, M., & Masood, A. (2023) · Diagnostics · doi:10.3390/diagnostics13193063 · Cited by 88 · see citing works
“Melanocytes are the cells in which melanoma develops when these cells overgrow and form a malignant tumor [11].”
BackgroundKandhro, I. A., Manickam, S., Fatima, K., Uddin, M., Malik, U. A., Naz, A., & Dandoush, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31488 · Cited by 82 · see citing works
Saeed, M., Naseer, A., Masood, H., Rehman, S. U., & Gruhn, V. (2023) · IEEE Access · doi:10.1109/access.2023.3332628 · Cited by 73 · see citing works
Naseri, H., & Safaei, A. A. (2025) · BMC Cancer · doi:10.1186/s12885-024-13423-y · Cited by 82 · see citing works
Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review
Hermosilla, P., Soto, R., Vega, E., Suazo, C., & Ponce, J. (2024) · Diagnostics · doi:10.3390/diagnostics14040454 · Cited by 66 · see citing works
Mehmood, A., Gulzar, Y., Ilyas, Q. M., Jabbari, A. M. A., Ahmad, M., & Iqbal, S. (2023) · Cancers · doi:10.3390/cancers15143604 · Cited by 47 · see citing works
Fusion of transformer attention and CNN features for skin cancer detection
Reis, H. Ç., & Turk, V. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112013 · Cited by 53 · see citing works
Saleh, N., Hassan, M. A., & Salaheldin, A. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-67424-9 · Cited by 53 · see citing works
SkinViT: A transformer based method for Melanoma and Nonmelanoma classification
Khan, S., & Khan, A. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0295151 · Cited by 50 · see citing works
Naeem, A., & Anees, T. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297667 · Cited by 48 · see citing works
Noronha, S., Mehta, M. A., Garg, D., Kotecha, K. V., & Abraham, A. (2023) · IEEE Access · doi:10.1109/access.2023.3339635 · Cited by 48 · see citing works
Al‐Waisy, A. S., Al-Fahdawi, S., Khalaf, M., Mohammed, M. A., Al-Attar, B., & Al-Andoli, M. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-15655-9 · Cited by 47 · see citing works
Riaz, S., Naeem, A., Malik, H., Naqvi, R. A., & Loh, W.-K. (2023) · Sensors · doi:10.3390/s23208457 · Cited by 45 · see citing works
Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets
Spolaôr, N., Lee, H., Mendes, A. I., Nogueira, C. V., Parmezan, A. R. S., Takaki, W. S. R., Coy, C. S. R., Wu, F. C., & Fonseca-Pinto, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16529-w · Cited by 42 · see citing works
Thapar, P., Rakhra, M., Alsaadi, M. A., Quraishi, A., Deka, A., & Ramesh, J. V. N. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100326 · Cited by 37 · see citing works
Kanchana, K., Kavitha, S., Anoop, K. J., & Chinthamani, B. (2024) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2024.25.5.1795 · Cited by 34 · see citing works
Tang, K., Su, J., Chen, R., Huang, R., Dai, M., & Li, Y. (2024) · Applied Sciences · doi:10.3390/app14104005 · Cited by 33 · see citing works
Behara, K., Bhero, E., & Agee, J. T. (2024) · Diagnostics · doi:10.3390/diagnostics14060636 · Cited by 35 · see citing works
Towards unbiased skin cancer classification using deep feature fusion
Abdulredah, A. A., Fadhel, M. A., Alzubaidi, L., Duan, Y., Kherallah, M., & Charfi, F. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02889-w · Cited by 31 · see citing works
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf329 · Cited by 30 · see citing works
Hussain, S. I., & Toscano, E. (2024) · Symmetry · doi:10.3390/sym16030366 · Cited by 34 · see citing works
Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives
Zbrzezny, A. M., & Krzywicki, T. (2025) · Applied Sciences · doi:10.3390/app15147856 · Cited by 35 · see citing works
Hameed, M., Zameer, A., & Raja, M. A. Z. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.050124 · Cited by 30 · see citing works
DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection
Reis, H. Ç., & Turk, V. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111182 · Cited by 27 · see citing works
The Smart deep learning based Model for Early Detection and Diagnosis of Melanoma
Mohammed, A. S., Neravetla, A. R., Samreen, S., Jiwani, N., & Logeshwaran, J. (2024) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs60521.2024.10498801 · Cited by 25 · see citing works
Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding
Wang, Z., Wang, C., Peng, L. H., Lin, K., Xue, Y., Chen, X., Bao, L., Liu, C., Zhang, J., & Xie, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-70231-x · Cited by 25 · see citing works
Next-generation approach to skin disorder prediction employing hybrid deep transfer learning
Gulzar, Y., Agarwal, S., Soomro, S., Kandpal, M., Turaev, S., Onn, C. W., Saini, S., & Bounsiar, A. (2025) · Frontiers in Big Data · doi:10.3389/fdata.2025.1503883 · Cited by 24 · see citing works
Halawani, H. T., Senan, E. M., Asiri, Y., Abunadi, I., Mashraqi, A. M., & Alshari, E. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-18570-1 · Cited by 24 · see citing works
Zareen, S. S., Hossain, M. S., Wang, J., & Kang, Y. (2025) · Precision Medical Sciences · doi:10.1002/prm2.12156 · Cited by 23 · see citing works
Hasnain, M. A., Malik, H., Asad, M. M., & Sherwani, F. (2023) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-08-2023-0230 · Cited by 23 · see citing works
Mahmud, M. A. A., Afrin, S., Mridha, M. F., Alfarhood, S., Che, D., & Safran, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-09938-4 · Cited by 27 · see citing works
MEFP-Net: A Dual-Encoding Multi-Scale Edge Feature Perception Network for Skin Lesion Segmentation
Hao, S., Yu, Z., Zhang, B., Dai, C., Fan, Z., Ji, Z., & Ганчев, И. (2024) · IEEE Access · doi:10.1109/access.2024.3467678 · Cited by 22 · see citing works
Verma, N., Ranvijay, & Yadav, D. K. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10275-y · Cited by 21 · see citing works
MSCDNet-based multi-class classification of skin cancer using dermoscopy images
Radhika, V., & Chandana, B. S. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1520 · Cited by 19 · see citing works
Quishpe-Usca, A., Cuenca-Dominguez, S., Arias-Viñansaca, A., Bosmediano-Angos, K., Villalba-Meneses, F., Ramírez-Cando, L., Tirado-Espín, A., Cadena-Morejón, C., Almeida-Galárraga, D. A., & Guevara, C. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1953 · Cited by 21 · see citing works
Enhancing skin lesion diagnosis with data augmentation techniques: a review of the state-of-the-art
Patil, A. Y., Mehto, A., & Nalband, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20145-7 · Cited by 20 · see citing works
Kumar, A., Agarwal, S., Gupta, S., & Agarwal, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17538-5 · Cited by 17 · see citing works
Ali, M. A., Dornaika, F., Arganda‐Carreras, I., Ali, H., & Karaouni, M. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4010035 · Cited by 17 · see citing works
Early-stage melanoma cancer diagnosis framework for imbalanced data from dermoscopic images
Khan, A. R., Mujahid, M., Alamri, F. S., Saba, T., & Ayesha, N. (2025) · Microscopy Research and Technique · doi:10.1002/jemt.24736 · Cited by 16 · see citing works
Yu, J., Pu, H., & Sun, D. (2025) · Food Control · doi:10.1016/j.foodcont.2025.111290 · Cited by 15 · see citing works
VGG16-based intelligent image analysis in the pathological diagnosis of IgA nephropathy
Chen, Y., Chen, Y., Fu, S., Yin, W., Liu, K., & Qian, S. (2023) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2023.100626 · Cited by 15 · see citing works
Hussain, S. I., & Toscano, E. (2025) · Mathematics · doi:10.3390/math13091480 · Cited by 15 · see citing works
Supriyanto, C., Salam, A., Zeniarja, J., & Wijaya, A. (2023) · Computation · doi:10.3390/computation11120246 · Cited by 14 · see citing works
Robot Assist Sign Language Recognition for Hearing Impaired Persons Using Deep Learning
Khan, K., Aslam, N., Abid, K., & Munir, S. (2023) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v11i1.1491 · Cited by 14 · see citing works
Resnet Transfer Learning For Enhanced Medical Image Classification In Healthcare
Varshney, N., Sharma, M. K., Saravanan, V., Shalini, N., Yadav, V. K., & Kumar, N. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489521 · Cited by 14 · see citing works
Khan, A. A., Sajid, M. Z., Khan, N. A., Youssef, A., & Abbas, Q. (2025) · Bioengineering · doi:10.3390/bioengineering12040326 · Cited by 14 · see citing works
Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction
Ismail, W. N., & Alsalamah, H. A. (2023) · Mathematics · doi:10.3390/math11163601 · Cited by 14 · see citing works
Naeem, A., Malik, H., din, M.-Z., Sadeghi‐Niaraki, A., Jeong, D., & Naqvi, R. A. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01952-w · Cited by 13 · see citing works
Gayatri, E., & Lakshminarayanan, A. S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124056 · Cited by 13 · see citing works
Systematic Review of Deep Learning Techniques in Skin Cancer Detection
Magalhaes, C., Mendes, J. G., & Vardasca, R. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4040121 · Cited by 13 · see citing works
Sarhan, A. M., Ali, H. A., Yasser, S., Gobara, M., Kandil, A. A., Sherif, G., & Moustafa, E. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00243-8 · Cited by 14 · see citing works
Gupta, A. K., Mathur, P., Sheth, F., Travieso, C. M., & Chaurasia, S. (2024) · Applied Computing and Geosciences · doi:10.1016/j.acags.2024.100192 · Cited by 12 · see citing works
Deep learning approaches for detecting malignant melanoma in dermoscopic imagery
Thakur, A., Mishra, S., Bhutani, M., Dash, P. P., Upadhyay, P. K., & Sahu, S. S. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02651-5 · Cited by 12 · see citing works
Saeed, A., Shehzad, K., Ahmed, S., & Azar, A. T. (2025) · IEEE Access · doi:10.1109/access.2025.3561240 · Cited by 11 · see citing works
Abir, S. I., Shoha, S., Shiam, S. A. A., Saimon, S. I., Islam, I., Mamun, M. A. I., Hossain, M. M., Rahman, S. M., & Khan, N. I. (2024) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2024.6.5.14 · Cited by 29 · see citing works
Nainwal, M., Reddy, Y. M. S., Dhaygude, A. D., Ramesh, G., Govindasamy, C., & Mouli, D. V. V. V. C. H. (2025) · Cybernetics & Systems · doi:10.1080/01969722.2025.2470786 · Cited by 10 · see citing works
Explainable AI-driven hybrid deep learning framework for accurate skin cancer diagnosis
Sakib, A. A., Swapno, S. M. M. R., Ahamed, F., Mohiuddin, A. B., Bhuiyan, M. I. H., Khan, S., Khushbu, K. G., Haque, R., Alahmadi, T. J., & Moni, M. A. (2026) · Digital Health · doi:10.1177/20552076261438923 · Cited by 10 · see citing works
Mirunalini, P., Desingu, K., Aswatha, S. M., Deepika, R., Deepika, V., & Jaisakthi, S. M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09964-9 · Cited by 10 · see citing works
Raju, A. S. N., Venkatesh, K., Padmaja, B., Kumar, C. N. S., Patnala, P. M., Lasisi, A., Islam, S. S. M., Razak, A., & Khan, W. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-81456-1 · Cited by 10 · see citing works
A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection
Kumar, K. A., & Vanmathi, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-85627-6 · Cited by 10 · see citing works
Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review
Vardasca, R., Mendes, J. G., & Magalhaes, C. (2024) · Journal of Imaging · doi:10.3390/jimaging10110265 · Cited by 10 · see citing works
Transforming Skin Cancer Diagnosis: A Deep Learning Approach with the Ham10000 Dataset
Priyeshkumar, A. T., Guruvare, S., Vasanth, T., & Selvan, P., V (2024) · Cancer Investigation · doi:10.1080/07357907.2024.2422602 · Cited by 10 · see citing works
Hassan, S. N., Khalil, M., Salahuddin, H., Naqvi, R. A., Jeong, D., & Lee, S. W. (2024) · Mathematics · doi:10.3390/math12223534 · Cited by 9 · see citing works
Alemu, M. G., & Zimale, F. A. (2025) · Environmental Monitoring and Assessment · doi:10.1007/s10661-025-13708-0 · Cited by 9 · see citing works
Explainable deep learning approaches for skin cancer diagnosis
Alrabai, A., Echtioui, A., & Kallel, F. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00554-w · Cited by 8 · see citing works
Skin Cancer Detection and Classification Through Medical Image Analysis Using EfficientNet
Das, S., & Addya, R. K. (2025) · NDT · doi:10.3390/ndt3040023 · Cited by 8 · see citing works
Ali, M. D., Iqbal, M. A., Lee, S., Duan, X., & Kim, S. K. (2025) · Applied Sciences · doi:10.3390/app152111689 · Cited by 8 · see citing works
Murali, P., & Mazumder, D. H. (2026) · Scientific Reports · doi:10.1038/s41598-026-46011-0 · Cited by 9 · see citing works
Performance Improvement of Melanoma Detection Using a Multi-Network System Based on Decision Fusion
El-Khatib, H., Ștefan, A.-M., & Popescu, D. (2023) · Applied Sciences · doi:10.3390/app131810536 · Cited by 8 · see citing works
Convolutional neural network-based skin cancer classification with transfer learning models
Oumoulylte, M., Alaoui, A. O., Farhaoui, Y., Allaoui, A. E., & Bahri, A. (2023) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2023.4.07 · Cited by 10 · see citing works
Khan, M. A., Hamza, A., Shabaz, M., Kadry, S., Rubab, S., Bilal, M. A., Akbar, M., & Manic, K. S. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05998-9 · Cited by 8 · see citing works
RetractedClassification of Skin Lesions using PyQt5 and Deep Learning Methods
Koishiyeva, D., Bissembayev, A., Iliev, T. B., Kang, J. W., & Mukasheva, A. (2024) · International Conference on Communications, Information, Electronic and Energy Systems (CIEES) · doi:10.1109/ciees62939.2024.10811274 · Cited by 7 · see citing works
Revolutionizing Dermatology with High-Accuracy Skin Condition Classification Through ResNet50
Kaur, A. (2024) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat62922.2024.10924079 · Cited by 7 · see citing works
ScNet: a lightweight CNN with depthwise and SE modules for skin lesion classification
Gulzar, Y., Ya’u, B. I., Alkanan, M., & Onn, C. W. (2025) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2025.2576198 · Cited by 7 · see citing works
Rahman, A. U., Albashrawi, M. A., Saqia, B., & Halim, Z. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109544 · Cited by 7 · see citing works
Eliwa, E. H. I. (2025) · International Journal of Breast Cancer · doi:10.1155/ijbc/4362941 · Cited by 6 · see citing works
Wang, L., Chi, Y., Li, J., & Yuan, X. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1674975 · Cited by 6 · see citing works
Melanoma classification using generative adversarial network and proximal policy optimization
Ju, X., Lin, C., Lee, S., & Wei, S. (2024) · Photochemistry and Photobiology · doi:10.1111/php.14006 · Cited by 6 · see citing works
An Effective Hybrid Model for Skin Cancer Detection Using Transfer Learning
Mohammed, H. J., Nafea, A. A., Almulla, H. K., Aliesawi, S. A. S., & AL-Ani, M. M. (2023) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese60595.2023.10468994 · Cited by 6 · see citing works
Giammarco, M. D., Santone, A., Cesarelli, M., Martinelli, F., & Mercaldo, F. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105675 · Cited by 5 · see citing works
Improved classification of oral cancer through a personalized transfer learning CNN architecture
Monani, U. J., Yun, T.-S., Sain, M., & Pattnaik, P. K. (2025) · Journal of Oral Biology and Craniofacial Research · doi:10.1016/j.jobcr.2025.10.002 · Cited by 5 · see citing works
Liu, W., Chen, J., & Lv, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130937 · Cited by 5 · see citing works
Patil, S., Paratakke, A., Kulkarni, A., Khomane, R. B., Panigrahi, G. R., & Singla, C. (2025) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect68904.2025.11318753 · Cited by 5 · see citing works
Skin Cancer Image Segmentation Based on Midpoint Analysis Approach
Saghir, U., Singh, S. K., & Hasan, M. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01106-w · Cited by 5 · see citing works
Khattar, S., & Bajaj, R. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.03.210 · Cited by 5 · see citing works
Computer-Aided Design for Skin Disease Identification and Categorization Using Deep Learning
Varalakshmi, K., V, & Revathi, A. R. (2023) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip61524.2023.10537681 · Cited by 5 · see citing works
Mui-zzud-din, Naeem, A., Malik, H., Arsalan, M., Jafar, A., & Ali, U. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109691 · Cited by 4 · see citing works
Ramos-Briceño, D. A., Pinto-Cuberos, J., Linfante, A. W., & Wilkerson, M. G. (2025) · Scientific Reports · doi:10.1038/s41598-025-31259-9 · Cited by 4 · see citing works
Pandala, M. L., & Periyanayagi, S. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-02096-z · Cited by 4 · see citing works
Skin Cancer Classification with Channel Attention and SMOTE Sampling: A Deep Learning Approach
Mahbub, M., & Ahsan, S. M. M. (2023) · International Conference on Electrical Information and Communication Technology (EICT) · doi:10.1109/eict61409.2023.10427872 · Cited by 4 · see citing works
Deep Learning for Automatic Diagnosis of Skin Cancer Using Dermoscopic Images
Krishna, S. R., Raj, V. K., Gudur, A. K., Jain, S., Deivasigamani, S., Tiwari, M., & Venkatesan, K. G. S. (2024) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2024.0392 · Cited by 4 · see citing works
A grad-CAM and deep learning based image classification for skin diseases
Muntasir, F., Uddin, M. A., Utsab, N. K. D., As-ad, J., & Tasnim, F. (2024) · International Conference on Computing Advancements (ICCA) · doi:10.1145/3723178.3723284 · Cited by 4 · see citing works
Caraka, R. E., Supardi, K., Gondhowiardjo, S. A., Isnaniawardhani, V., Gio, P. U., Chen, R.-C., & Pardamean, B. (2025) · Journal of Healthcare Leadership · doi:10.2147/jhl.s550933 · Cited by 3 · see citing works
Deep Neural Networks for Skin Cancer Classification: Analysis of Melanoma Cancer Data
Afrifa, S., Varadarajan, V., Appiahene, P., Zhang, T., Gyamfi, D., & Gyening, R.-M. O. M. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.1.1-11 · Cited by 3 · see citing works
Muthukrishnan, R., Balasubramaniam, A., Krishnasamy, V., & Ravichandran, S. K. (2025) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.70043 · Cited by 3 · see citing works
Skin Cancer Classifier: Performance Enhancement Using Deep Learning Models
Mishra, S., & Agarwal, M. (2025) · doi:10.1109/icsc64553.2025.10969043 · Cited by 3 · see citing works
Patil, P. R., & Tandon, R. (2025) · Journal of Innovative Image Processing · doi:10.36548/jiip.2025.3.019 · Cited by 3 · see citing works
- Authors
- Tahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R. A., & Lee, S.-W.
- Affiliations
- National College of Business Administration and Economics (PK)
- University of Management and Technology (PK)
- Sejong University (KR)
- Sungkyunkwan University (KR)
- Corresponding author
- Naqvi, Rizwan Ali
- Lee, Seung-Won
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2023-04-06
- Volume
- 15
- Issue
- 7
- Pages
- 2179
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC10093058
- OpenAlex
- W4362665647
- Cited by
- 240
- References
- 76
- Citations by year
- 2026: 53 · 2025: 88 · 2024: 68 · 2023: 31
- FWCI
- 29.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and ICT (MSIT) (NRF2022R1G1A1010226, NRF2021R1I1A2059735)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Epidemiology › Nonmelanoma Skin Cancer Studies
- Keywords
- skin cancer classification, dermoscopic images, melanoma, basal cell carcinoma, squamous cell carcinoma, melanocytic nevi, convolutional neural networks, early detection, class imbalance, SMOTE-Tomek, ISIC 2020, HAM10000, dermis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
