The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Skin lesion classification of dermoscopic images using machine learning and convolutional neural network
Open accessAbstract
Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners.
Overview
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
Figures
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions (14–22).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Later, CNN-based automatic feature extraction (14) and transfer learning with pretrained models (15, 18) improved representation learning.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Most prior studies address this issue through conventional image-level augmentation (7, 14, 15, 17, 18, 20, 21, 23), which increases data volume but does not introduce genuine semantic diversity.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Class imbalance remains a central challenge in seven-class skin lesion classification using HAM10000.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“In the classification stage, most prior studies relied primarily on the fully connected layer for final classification (7, 14, 16–18, 20).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [14]
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.
Citations
Cites
2 retracted
RETRACTED ARTICLE: Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis
Gadekallu, T. R., Maddikunta, P. K. R., Lakshmanna, K., Rajput, D. S., Kaluri, R., & Srivastava, G. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-019-00327-1 · Cited by 407
Self-citationRetractedRETRACTED ARTICLE: Skin lesion classification using decision trees and random forest algorithms
Dhivyaa, C. R., Sangeetha, K., -, M. B., Amaran, S., Vetriselvi, T., & Johnpaul, P. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02675-8 · Cited by 90
RetractedTschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM
Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021) · Sensors · doi:10.3390/s21082852 · Cited by 763
Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., & Ellinger, I. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105475 · Cited by 326
Skin cancer diagnosis based on optimized convolutional neural network
Zhang, N., Cai, Y.-X., Wang, Y.-Y., Tian, Y., Wang, X., & Badami, B. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101756 · Cited by 325
A multi-class skin Cancer classification using deep convolutional neural networks
Chaturvedi, S. S., Tembhurne, J. V., & Diwan, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09388-2 · Cited by 297
Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features
Yu, Z., Jiang, X., Zhou, F., Qin, J., Ni, D., Chen, S., Lei, B., & Wang, T. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2866166 · Cited by 253
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
Convolutional Neural Networks Using MobileNet for Skin Lesion Classification
Sae-Lim, W., Wettayaprasit, W., & Aiyarak, P. (2019) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse.2019.8864155 · Cited by 233
Research works printed but not linked: 1
- Zaqout, I. Diagnosis of skin lesions based on dermoscopic images using image processing techniques. J. Pattern Recogn. Sel. Methods Appl. 2019, 189–204 (2019).
Cited by
Sorted by citations
Islam, T., Hafiz, M. S., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100340 · Cited by 234
A survey, review, and future trends of skin lesion segmentation and classification
Hasan, M. K., Ahamad, M. A., Yap, C. H., & Yang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106624 · Cited by 161
Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review
Debelee, T. G. (2023) · Diagnostics · doi:10.3390/diagnostics13193147 · Cited by 140
Musthafa, M. M., Mahesh, T. R., Kumar, V. V., & Guluwadi, S. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01356-8 · Cited by 140
Alsahafi, Y. S., Kassem, M. A. A., & Hosny, K. M. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00769-6 · Cited by 127
Lilhore, U. K., Simaiya, S., Sharma, Y. K., Kaswan, K. S., Rao, K. B. V. B., Rao, V. V. R. M., Baliyan, A., Bijalwan, A., & Alroobaea, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-54212-8 · Cited by 115
Ali, S. N., Ahmed, M. T., Jahan, T., Paul, J., Sani, S. M. S., Noor, N., Asma, A. N., & Hasan, T. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106742 · Cited by 117
Zhang, J., Zhong, F., He, K., Ji, M., Li, S., & Li, C. (2023) · Diagnostics · doi:10.3390/diagnostics13233506 · Cited by 106
Naeem, A., Anees, T., Khalil, M., Zahra, K., Naqvi, R. A., & Lee, S. W. (2024) · Mathematics · doi:10.3390/math12071030 · Cited by 107
Analysis and Design of Surgical Instrument Localization Algorithm
Lu, S., Yang, J., Yang, B., Yin, Z., Liu, M., Yin, L., & Zheng, W. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.027417 · Cited by 95
Skin cancer detection using dermoscopic images with convolutional neural network
Nawaz, K., Zanib, A., Shabir, I., Li, J., Wang, Y., Mahmood, T., & Rehman, A. U. (2025) · Scientific Reports · doi:10.1038/s41598-025-91446-6 · Cited by 90
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
Zafar, M., Amin, J., Sharif, M., Anjum, M. A., Mallah, G. A., & Kadry, S. (2023) · Mathematics · doi:10.3390/math11020364 · Cited by 53
Mukadam, S. B., & Patil, H. Y. (2023) · Applied Sciences · doi:10.3390/app13021210 · Cited by 53
Naeem, A., & Anees, T. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297667 · Cited by 48
Machine Learning Methods in Skin Disease Recognition: A Systematic Review
Sun, J., Yao, K., Huang, G., Zhang, C., Leach, M. P., Huang, K., & Yang, X. (2023) · Processes · doi:10.3390/pr11041003 · Cited by 48
Karthik, R., Menaka, R., Atre, S., Cho, J., & Easwaramoorthy, S. V. (2024) · IEEE Access · doi:10.1109/access.2024.3485507 · Cited by 45
Baig, A. R., Abbas, Q., Almakki, R. S., Ibrahim, M. E. A., AlSuwaidan, L., & Ahmed, A. E. S. (2023) · Diagnostics · doi:10.3390/diagnostics13030385 · Cited by 44
Tajerian, A., Kazemian, M., Tajerian, M., & Malayeri, A. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284437 · Cited by 43
Lyakhova, U. A., & Lyakhov, P. A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108742 · Cited by 42
Efat, A. H., Hasan, S. M. M., Uddin, M. P., & Mamun, M. A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309430 · Cited by 38
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 37
Hussain, S. I., & Toscano, E. (2024) · Symmetry · doi:10.3390/sym16030366 · Cited by 34
A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases
Badr, M., Elkasaby, A., Alrahmawy, M. F., & El‐Metwally, S. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01300-w · Cited by 34
Arunachalam, R., Sunitha, G., Shukla, S. K., Pandey, S. N., Urooj, S., & Rawat, S. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01890-x · Cited by 34
Ahmad, I., Amin, J., Lali, M. I. U., Abbas, F., & Sharif, M. I. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106084 · Cited by 30
MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features
Nirupama, & Virupakshappa, V. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01271-y · Cited by 30
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
Islam, R., Sultana, A., Tuhin, M. N., Saikat, M. S. H., & Islam, M. R. (2023) · Journal of Healthcare Engineering · doi:10.1155/2023/6992441 · Cited by 28
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images
Campana, M. G., Colussi, M., Delmastro, F., Mascetti, S., & Pagani, E. (2024) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101874 · Cited by 28
Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions
Sönmez, A. F., Kaman, S. Ç., ÇEREZCİ, F., Kotan, M., Delibaşoğlu, İ., & Çit, G. (2023) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1314638 · Cited by 27
Shapiro, J., & Lyakhovitsky, A. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.020 · Cited by 26
Karthik, R., Thayumanaswamy, I., Radhakrishnan, M., Won, D., & Lingaswamy, S. (2024) · Diagnostics · doi:10.3390/diagnostics14131338 · Cited by 26
A Suitable Approach for Classifying Skin Disease Using Deep Convolutional Neural Network
Sharma, G., Anand, V., & Kumar, V. (2023) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf58270.2023.10235079 · Cited by 25
Karaddi, S. H., Sharma, L. D., & Bhattacharya, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3445286 · Cited by 24
Dual scale light weight cross attention transformer for skin lesion classification
Yadav, D. P., Sharma, B., Chauhan, S., Webber, J. L., & Mehbodniya, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0312598 · Cited by 24
Zareen, S. S., Hossain, M. S., Wang, J., & Kang, Y. (2025) · Precision Medical Sciences · doi:10.1002/prm2.12156 · Cited by 23
Rezk, E., Haggag, M., Eltorki, M., & El-Dakhakhni, W. W. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100259 · Cited by 24
Skin cancer classification using machine learning
Tchema, R. B., Polycarpou, A. C., & Nestoros, M. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20595-7 · Cited by 21
Verma, N., Ranvijay, & Yadav, D. K. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10275-y · Cited by 21
Deep Learning-Based Classification of Melanoma and Non-Melanoma Skin Cancer
Alabdulkreem, E. A., Elmannai, H., Saad, A., Kamil, I. S., & Elaraby, A. E. (2024) · Traitement du signal · doi:10.18280/ts.410117 · Cited by 21
Deep Learning Approaches for Skin Lesion Detection
Vieira, J., Mendonça, F., & Morgado‐Dias, F. (2025) · Electronics · doi:10.3390/electronics14142785 · Cited by 20
Hamida, S., Gannour, O. E., Lamalem, Y., Saleh, S., Lamrani, D., & Cherradi, B. (2023) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset57153.2023.10152883 · Cited by 19
Refining skin lesions classification performance using geometric features of superpixels
Moldovanu, S., Miron, M., Rusu, C.-G., Biswas, K. C., & Moraru, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-38706-5 · Cited by 19
Livestock animal skin disease detection and classification using deep learning approaches
Girmaw, D. W. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107334 · Cited by 19
Shafin, M. S., Efat, A. H., Hasan, S. M. M., Jannat, N., Oishe, M. R., Mitu, M., & Zaman, M. U. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441280 · Cited by 18
Mazhar, F., Aslam, N., Naeem, A., Ahmad, H., Fuzail, M., & Imran, M. (2025) · Journal of Computing Theories and Applications · doi:10.62411/jcta.11954 · Cited by 21
Gannour, O. E., Hamida, S., Lamalem, Y., Mahjoubi, M. A., Cherradi, B., & Raihani, A. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v13i1.5999 · Cited by 17
Arjun, K. P., Kumar, K. S., Dhanaraj, R. K., Ravi, V., & Kumar, T. (2024) · Microscopy Research and Technique · doi:10.1002/jemt.24559 · Cited by 18
Mukhlif, Y. A., Ramaha, N. T. A., Hameed, A. A., Salman, M., Yon, D. K., Fitriyani, N. L., Syafrudin, M., & Lee, S. W. (2024) · Mathematics · doi:10.3390/math12071049 · Cited by 17
Gómez-Martínez, V., Chushig-Muzo, D., Veierød, M. B., Granja, C., & Soguero-Ruíz, C. (2024) · BioData Mining · doi:10.1186/s13040-024-00397-7 · Cited by 18
Attention-Based Deep Feature Aggregation Network for Skin Lesion Classification
Yasir, S. M., & Kim, H. (2025) · Electronics · doi:10.3390/electronics14122364 · Cited by 16
Badhon, S. M. S. I., Khushbu, S. A., Shaqib, S., Ali, M. A., Anik, A. H., & Hossain, K. S. M. T. (2025) · Digital Health · doi:10.1177/20552076251404523 · Cited by 17
The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning
Samara, G., Al-Daoud, E., Sweerky, N. A., & Alzu’bi, D. (2023) · Advances in Multimedia · doi:10.1155/2023/2642558 · Cited by 16
Brain Tumor Detection using Machine Learning and Convolutional Neural Network
Shetty, B., Fernandes, R., Rodrigues, A. P., & Vijaya, P. (2022) · International Conference on Artificial Intelligence and Data Engineering (AIDE) · doi:10.1109/aide57180.2022.10060254 · Cited by 15
Self-citationEnhancing Skin Disease Diagnosis Through Convolutional Neural Networks and YOLO v8 Object Detection
Talukdar, R., Dutta, S., & Das, S. (2023) · International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) · doi:10.1109/iementech60402.2023.10423482 · Cited by 14
Pillai, R., Sharma, N., Gupta, S., Gupta, D., Juneja, S., Malik, S., Qin, H. H., Alqahtani, M. S., & Ksibi, A. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1436470 · Cited by 14
Multimodal dual-stage feature refinement for robust skin lesion classification
Khurshid, M., Singh, R., & Vatsa, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-14839-7 · Cited by 13
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
A survey of recent advances in analysis of skin images
Gupta, P., Nirmal, J. H., & Mehendale, N. D. (2024) · Evolutionary Intelligence · doi:10.1007/s12065-024-00977-w · Cited by 13
Rasel, M. A., Kareem, S. A., & Obaidellah, U. H. B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109250 · Cited by 13
Mupparaju, S. B., & Reddy, R. (2024) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns62112.2024.10691081 · Cited by 12
Deep Learning-Based Web Application for Automated Skin Lesion Classification and Analysis
Aksoy, S., Demircioğlu, P., & Böğrekçi, İ. (2025) · Dermato · doi:10.3390/dermato5020007 · Cited by 12
Niranjana, R., Hemadarshana, T., Ilakkya, S., Krishnan, R. S., Epziba, J. J., & Preetha, T. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575230 · Cited by 11
Isewon, I., Alagbe, E., & Oyelade, J. O. (2025) · F1000Research · doi:10.12688/f1000research.156122.1 · Cited by 11
Ramesh, B., & Lakshmanna, K. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.028944 · Cited by 11
Self-citationAyyappan, G., & Senthil, K. (2025) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis66685.2025.11315535 · Cited by 10
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
Efat, A. H., Hasan, S. M. M., Uddin, M. P., & Emon, F. H. (2025) · Digital Health · doi:10.1177/20552076241312936 · Cited by 10
Tabibi, S. T., Nikravanshalmani, A., & Saboohi, H. (2024) · IEEE Access · doi:10.1109/access.2024.3442827 · Cited by 9
Classification of Dermoscopic Images Using CNN-SVM
Minarno, A. E., Fadhlan, M., Munarko, Y., & Chandranegara, D. R. (2024) · JOIV International Journal on Informatics Visualization · doi:10.62527/joiv.8.2.2153 · Cited by 8
Skin Disease Classification Using Deep Learning
Rangaswamy, S., Tantry, S. S., & Lal, T. S. (2024) · National Academy Science Letters · doi:10.1007/s40009-024-01523-z · Cited by 8
Asha, S., Sreeraj, R., Nambiar, S. K., & Cherian, A. K. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20854-7 · Cited by 8
Identification and Classification of Skin Diseases with Erythema Using YOLO Algorithm
Santhosh Kumar, C., Devangana, K. A., Abirami, P., Prasanna, M., & Aravind, S. H. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-4626-6_49 · Cited by 8
Dual Stream Feature Fusion 3D Network for supraspinatus tendon tear classification
Miao, S. J., Wang, D., Yang, X., Liu, Z., Shen, X., Hao, D., Zhou, C., & Cui, J. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102580 · Cited by 7
Almutairi, S. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-01319-1 · Cited by 7
Effective skin cancer classification by modified and optimized inception-ResNet-V2 model
Krishnamoorthy, U., Vanithamani, R., Priya, L., Harini, K., Dadi, M., Dixit, S., Chan, C. K., Kumar, S., & Siddiqui, M. I. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-25480-9 · Cited by 7
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 8
Duhaim, A. M., Baqer, N. S., & Fadhel, M. A. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.108914 · Cited by 7
Automated Adjustment of PPE Masks Using IoT Sensor Fusion
Manchanda, A., Lee, K., Poznanski, G. D., & Hassani, A. (2023) · Sensors · doi:10.3390/s23031711 · Cited by 7
Skin cancer risk self-assessment using AI as a mass screening tool
Ukharov, A. O., Леонидовна, Ш. И., Александровна, К. И., Garanina, O. E., Uskova, K. A., Mironycheva, A. M., & Stepanova, Y. L. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101223 · Cited by 7
An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
Li, Z., Ji, Q. H., Yang, X., Zhou, Y., & Zhi, S. (2023) · Applied Sciences · doi:10.3390/app131810076 · Cited by 7
Shirabayashi, J. V., Braga, A. S. M., & da Silva, J. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08430-2 · Cited by 7
Acne Skin Disease Detection Using Convolutional Neural Network Model
Bansal, K., Saini, M. L., Rahul, R., Bhardwaj, K., & Prajapati, L. (2023) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs59847.2023.10389831 · Cited by 7
Muthulakshmi, K., Maruthuperumal, S., & Kumari, G. R. N. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108422 · Cited by 6
Khan, M. A. A., Tahir, M., Irtaza, S. A., Mughal, M. R., & Yousaf, M. N. (2025) · IEEE Access · doi:10.1109/access.2025.3641868 · Cited by 6
Ayyappan, G., & Senthil, K. (2025) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat66372.2025.11368562 · Cited by 6
Skin Disease Classification using Transfer Learning with a Modified ResNet-50 Architecture
Priya Bhaminie., S., & Yogesh, C. K. (2024) · International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) · doi:10.1109/adics58448.2024.10533593 · Cited by 6
Can Vision Transformers Be the Next State-of-the-Art Model for Oncology Medical Image Analysis?
Venugopal, S. (2024) · AI in Precision Oncology · doi:10.1089/aipo.2024.0023 · Cited by 6
Diagnosis of Skin Cancer Using Machine Learning and Image Processing Techniques
Kaler, P., Kodli, S. B., & Anakal, S. S. (2022) · International Journal of Education and Management Engineering · doi:10.5815/ijeme.2022.05.05 · Cited by 6
Proposed Convolution Neural Network for Skin Cancer Diagnosis and Classification
Pillai, R., Sharma, N. V., & Gupta, R. (2023) · International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON) · doi:10.1109/reedcon57544.2023.10151029 · Cited by 6
Skin Cancer Classification and Segmentation Using Deep Learning
Badawi, M., Elgohary, R., Tarek, M., EzzAlRegal, M., Ahmed, A., Samir, A., & Ehab, N. (2024) · International Journal of Telecommunications · doi:10.21608/ijt.2024.280957.1045 · Cited by 6
Zhao, J., Liu, T., & Sun, L. (2025) · Electronics · doi:10.3390/electronics14050912 · Cited by 5
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
Delower, H. M. L., Mohammad, T., Prity, S. J., Islam, M. B., Mansoor, M. T., Rhythm, E. R., & Rasel, A. A. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441420 · Cited by 5
Synergistic Skin Cancer Classification: Vision Transformer alongside MobileNetV2
Manoharan, R. K., Priyanga, S., Anusha, J. S., Chatiyode, V., Santiago, J., & Revathi, P. (2024) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit61985.2024.10627580 · Cited by 5
Hybrid Algorithm Using CNN and LSTM to Improve Performance in Skin Lesion Image Classification
Putri, I. R., Amalia, A., & Abidin, T. F. (2024) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic64337.2024.10956620 · Cited by 5
Brain Tumour Detection Using VGG‐Based Feature Extraction With Modified DarkNet‐53 Model
Trisheela, S., Fernandes, R., Rodrigues, A. P., Supreeth, S., Ambika, B. J., Pareek, P. K., Godi, R. K., & Shruthi, G. (2025) · International Journal of Biomedical Imaging · doi:10.1155/ijbi/5535505 · Cited by 5
Self-citationStepwise self-knowledge distillation for skin lesion image classification
Zheng, J., Xie, K., Zhang, D., Lv, Z., & Yu, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-10717-4 · Cited by 5
Metadata
- Authors
- Shetty, B., Fernandes, R., Rodrigues, A. P., Chengoden, R., Bhattacharya, S., & Lakshmanna, K.
- Affiliations
- Nitte University (IN)
- Vellore Institute of Technology University (IN)
- Corresponding author
- Lakshmanna, Kuruva
- Venue
- Scientific Reports
- Published
- 2022-10-28
- Volume
- 12
- Issue
- 1
- Pages
- 18134-18134
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9616944
- OpenAlex
- W4307511304
- Cited by
- 290
- References
- 35
- Citations by year
- 2026: 61 · 2025: 101 · 2024: 78 · 2023: 46 · 2022: 4
- FWCI
- 23.96
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-12-19)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- VIT University (632014)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- skin lesion classification, dermoscopic images, pigmented skin lesions, skin cancer detection, HAM10000 dataset, data augmentation, k-fold cross-validation, convolutional neural networks, machine learning classification
- MeSH terms
- Machine Learning, Algorithms, Humans, Skin Diseases, Skin Neoplasms, Neural Networks, Computer
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
