AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions

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

Overview of Next‐Generation Sequencing Technologies

Slatko, B. E., Gardner, A. F., & Ausubel, F. M. (2018) · Current Protocols in Molecular Biology · doi:10.1002/cpmb.59 · Cited by 968

Next-generation sequencing technologies: An overview

Hu, T., Chitnis, N., Monos, D., & Dinh, A. (2021) · Human Immunology · doi:10.1016/j.humimm.2021.02.012 · Cited by 949

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

Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

Han, S. S., Kim, M. S., Lim, W., Park, G., Park, I., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.01.028 · Cited by 740

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Brinker, T. J., Hekler, A., Utikal, J. S., Grabe, N., Schadendorf, D., Klode, J., Berking, C., Steeb, T., Enk, A. H., & Kalle, C. V. (2018) · Journal of Medical Internet Research · doi:10.2196/11936 · Cited by 528

Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities

Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104065 · Cited by 476

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019) · Cancers · doi:10.3390/cancers11091235 · Cited by 437

The skin cancer classification using deep convolutional neural network

Dorj, U.-O., Lee, K.-K., Choi, J., & Lee, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5714-1 · Cited by 422

Superior skin cancer classification by the combination of human and artificial intelligence

Hekler, A., Utikal, J. S., Enk, A. H., Hauschild, A., Weichenthal, M., Maron, R. C., Berking, C., Haferkamp, S., Klode, J., Schadendorf, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.07.019 · Cited by 368

Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network

Alom, M. Z., Yakopcic, C., Nasrin, M. S., Taha, T. M., & Asari, V. K. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00182-7 · Cited by 353

Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art

Adegun, A. A., & Viriri, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09865-y · Cited by 340

Ethical dimensions of using artificial intelligence in health care

(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.121 · Cited by 328

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

Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis

Fujisawa, Y., Otomo, Y., Ogata, Y., Nakamura, Y., Fujita, R., Ishitsuka, Y., Watanabe, R., Okiyama, N., Ohara, K., & Fujimoto, M. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16924 · Cited by 324

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge

Veta, M., Heng, Y. J., Stathonikos, N., Bejnordi, B. E., de Beça, F. F., Wollmann, T., Rohr, K., Shah, M., Wang, D., Rousson, M., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.012 · Cited by 309

A decision support system for multimodal brain tumor classification using deep learning

Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., & Raza, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00321-0 · Cited by 305

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges

Saba, T. (2020) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2020.06.033 · Cited by 298

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

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan, S. Y., Reddy, V. D., Myers, B. A., Thibodeaux, Q. G., Brownstone, N. D., & Liao, W. J. (2020) · Dermatology and Therapy · doi:10.1007/s13555-020-00372-0 · Cited by 285

Ultraviolet Radiation Exposure and Its Impact on Skin Cancer Risk

Watson, M., Holman, D. M., & Maguire-Eisen, M. (2016) · Seminars in Oncology Nursing · doi:10.1016/j.soncn.2016.05.005 · Cited by 280

Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study

Nelson, C. A., Pérez-Chada, L. M., Creadore, A., Li, S. J., Lo, K., Manjaly, P., Pournamdari, A. B., Tkachenko, E., Barbieri, J. S., Ko, J. M., et al. (2020) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.5014 · Cited by 278

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020) · Biomolecules · doi:10.3390/biom10081123 · Cited by 276

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.06.049 · Cited by 267

Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers

Murugan, A., Nair, S., & Kumar, K. P. S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1400-8 · Cited by 265

Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images

Kadampur, M. A., & Alriyaee, S. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100282 · Cited by 265

Skin cancer detection from dermoscopic images using deep learning and fuzzy k ‐means clustering

Nawaz, M., Mehmood, Z., Nazir, T., Naqvi, R. A., Rehman, A. U., Iqbal, M., & Saba, T. (2021) · Microscopy Research and Technique · doi:10.1002/jemt.23908 · Cited by 263

Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer

Khan, M. Q., Hussain, A., Rehman, S. U., Khan, U. M., Maqsood, M., Mehmood, K., & Khan, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2926837 · Cited by 262

Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction

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

Application of decision tree-based ensemble learning in the classification of breast cancer

Ghiasi, M. M., & Zendehboudi, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104089 · Cited by 249

Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., Chae, J. B., Huh, C. H., & Na, J.-I. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.01.019 · Cited by 248

Characteristics of publicly available skin cancer image datasets: a systematic review

Wen, D., Khan, S. M., Ji Xu, A., Ibrahim, H., Smith, L., Caballero, J., Zepeda, L., de Blas Perez, C., Denniston, A. K., Liu, X., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00252-1 · Cited by 243

A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications

Ak, M. F. (2020) · Healthcare · doi:10.3390/healthcare8020111 · Cited by 239

Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier

Balaji, V., Suganthi, S. T., Rajadevi, R., Kumar, V. K., Balaji, B. S., & Pandiyan, S. (2020) · Measurement · doi:10.1016/j.measurement.2020.107922 · Cited by 192

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

Naeem, A., Farooq, M. S., Khelifi, A., & Abid, A. (2020) · IEEE Access · doi:10.1109/access.2020.3001507 · Cited by 189

A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization

Sayed, G. I., Soliman, M., & Hassanien, A. E. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104712 · Cited by 188

Skin cancer detection and classification using machine learning

Monika, M. K., Vignesh, N. A., Kumari, C. U., Kumar, M., & Lydia, E. L. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.07.366 · Cited by 184

Skin disease diagnosis with deep learning: a review

Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178

Machine Learning and Its Application in Skin Cancer

Das, K., Cockerell, C. J., Patil, A. D., Pietkiewicz, P., Giulini, M., Grabbe, S., & Goldust, M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182413409 · Cited by 175

Diagnosis of skin cancer using machine learning techniques

Murugan, A., Nair, S., Preethi, A. A. P., & Kumar, K. P. S. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103727 · Cited by 174

An approach for multiclass skin lesion classification based on ensemble learning

Rahman, Z., Hossain, M. S., Islam, M. R., Hasan, M. M., & Hridhee, R. A. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100659 · Cited by 174

Skin Cancer Concerns in People of Color: Risk Factors and Prevention

Gupta, A., Bharadwaj, M., & Mehrotra, R. (2016) · Europe PMC (PubMed Central) · doi:10.22034/apjcp.2016.17.12.5257 · Cited by 169

Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models

Tan, T. Y., Li, Z., & Lim, C. P. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105725 · Cited by 163

Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review

Painuli, D., Bhardwaj, S., & Köse, U. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105580 · Cited by 163

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Afza, F., Sharif, M. I., Khan, M. A., Tariq, U., Yong, H.-S., & Cha, J. (2022) · Sensors · doi:10.3390/s22030799 · Cited by 159

Melanoma Skin Cancer Detection using Image Processing and Machine Learning

M, V. M. (2019) · International Journal of Trend in Scientific Research and Development · doi:10.31142/ijtsrd23936 · Cited by 155

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tufail, A. B., Ma, Y., Kaabar, M. K. A., Martínez, F., Junejo, A. R., Ullah, I., & Khan, R. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/9025470 · Cited by 151

Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network

Han, S. S., Moon, I. J., Lim, W., Suh, I. S., Lee, S. Y., Na, J., Kim, S. H., & Chang, S. E. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.3807 · Cited by 150

Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks

Toğaçar, M., Cömert, Z., & Ergen, B. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110714 · Cited by 148

Optical coherence tomography for diagnosing skin cancer in adults

di Ruffano, L. F., Dinnes, J., Deeks, J. J., Chuchu, N., Bayliss, S. E., Davenport, C. F., Takwoingi, Y., Godfrey, K., O'Sullivan, C., Matin, R., et al. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013189 · Cited by 145

Skin Lesion Classification using Machine Learning Algorithms

Özkan, İ. A., & Köklü, M. (2017) · International Journal of Intelligent Systems and Applications in Engineering · doi:10.18201/ijisae.2017534420 · Cited by 143

An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network

Al–antari, M. A., Al‐masni, M. A., Park, S., Park, J., Metwally, M. K., Kadah, Y. M., Han, S., & Kim, T.-S. (2017) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-017-0321-6 · Cited by 140

A two‐stream deep neural network‐based intelligent system for complex skin cancer types classification

Khan, M. A., Sharif, M. Y., Akram, T., Kadry, S., & Hsu, C. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22691 · Cited by 127

Enhancing Skin Cancer Diagnosis with Dermoscopy

Wolner, Z. J., Yélamos, O., Liopyris, K., Rogers, T., Marchetti, M. A., & Marghoob, A. A. (2017) · Dermatologic Clinics · doi:10.1016/j.det.2017.06.003 · Cited by 116

An ensemble classification approach for melanoma diagnosis

Schaefer, G., Krawczyk, B., Celebi, M. E., & Iyatomi, H. (2014) · Memetic Computing · doi:10.1007/s12293-014-0144-8 · Cited by 113

Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis

Mobiny, A., Singh, A., & Nguyen, H. V. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8081241 · Cited by 112

Skin cancer: findings and role of high-resolution ultrasound

Catalano, O., Alfageme, F., Varelli, C., Bard, R. L., Corvino, A., & Wortsman, X. (2019) · Journal of Ultrasound · doi:10.1007/s40477-019-00379-0 · Cited by 108

Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network

Patil, R. S., & Biradar, N. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00403-x · Cited by 105

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network

Roffman, D., Hart, G., Girardi, M., Ko, C. J., & Deng, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-19907-9 · Cited by 104

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

Abdar, M., Acharya, U. R., Sarrafzadegan, N., & Makarenkov, V. (2019) · IEEE Access · doi:10.1109/access.2019.2953920 · Cited by 103

Audio-Visual Emotion Recognition Using Big Data Towards 5G

Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. N. (2016) · Mobile Networks and Applications · doi:10.1007/s11036-016-0685-9 · Cited by 97

Classification of skin cancer using convolutional neural networks analysis of Raman spectra

Bratchenko, I. А., Bratchenko, L. A., Khristoforova, Y. А., Moryatov, A. A., Kozlov, S. V., & Zakharov, V. P. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106755 · Cited by 90

The limitations of dermoscopy: false‐positive and false‐negative tumours

Papageorgiou, V. P., Apalla, Z., Sotiriou, E., Papageorgiou, C., Lazaridou, E., Vakirlis, S., Ioannides, D., & Lallas, A. (2018) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.14782 · Cited by 87

Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Maron, R. C., Utikal, J. S., Hekler, A., Hauschild, A., Sattler, E., Sondermann, W., Haferkamp, S., Schilling, B., Heppt, M. V., Jansen, P., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/18091 · Cited by 87

Teledermatology: idea, benefits and risks of modern age - a systematic review based on melanoma

Pala, P., Bergler-Czop, B. S., & Gwiżdż, J. (2020) · Advances in Dermatology and Allergology · doi:10.5114/ada.2020.94834 · Cited by 83

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Höhn, J., Hekler, A., Krieghoff‐Henning, E., Kather, J. N., Utikal, J. S., Meier, F. E., Gellrich, F. F., Hauschild, A., French, L. E., Schlager, J. G., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/20708 · Cited by 74

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Tschandl, P. (2018) · Harvard Dataverse · doi:10.7910/dvn/dbw86t · Cited by 71

Classification of Melanoma Skin Cancer using Convolutional Neural Network

Refianti, R., Mutiara, A. B., & Poetri, R. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100353 · Cited by 67

Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis

Nauta, M., Walsh, R., Dubowski, A., & Seifert, C. (2021) · Diagnostics · doi:10.3390/diagnostics12010040 · Cited by 67

Legal concerns in health-related artificial intelligence: a scoping review protocol

Silva, M. D., Horsley, T., Singh, D. A., Silva, E. D., Ly, V., Thomas, B., Daniel, R. C., Chagal-Feferkorn, K., Iantomasi, S., White, K., et al. (2022) · Systematic Reviews · doi:10.1186/s13643-022-01939-y · Cited by 66

Remote Diagnosis and Triaging Model for Skin Cancer Using EfficientNet and Extreme Gradient Boosting

Khan, I. U., Aslam, N., Anwar, T., Aljameel, S. S., Ullah, M., Khan, R., Rehman, A., & Akhtar, N. (2021) · Complexity · doi:10.1155/2021/5591614 · Cited by 63

Noninvasive Imaging Tools in the Diagnosis and Treatment of Skin Cancers

Bakos, R. M., Blumetti, T. P., Roldán–Marín, R., & Salerni, G. (2018) · American Journal of Clinical Dermatology · doi:10.1007/s40257-018-0367-4 · Cited by 57

Bag of feature and support vector machine based early diagnosis of skin cancer

Arora, G., Dubey, A. K., Jaffery, Z. A., & Rocha, Á. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05212-y · Cited by 57

Deep learning for dermatologists: Part I. Fundamental concepts

Murphree, D. H., Puri, P., Shamim, H., Bezalel, S. A., Drage, L. A., Wang, M., Pittelkow, M. R., Carter, R. E., Davis, M. D. P., Bridges, A. G., et al. (2020) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.05.056 · Cited by 54

Microscopic Blood Cell Classification Using Inception Recurrent Residual Convolutional Neural Networks

Alom, M. Z., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2018.8556737 · Cited by 53

Long short-term memory model – A deep learning approach for medical data with irregularity in cancer predication with tumor markers

Wu, X., Wang, H., Shi, P., Sun, R., Wang, X., Luo, Z., Zeng, F., Lebowitz, M., Lin, W.-Y., Lu, J., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105362 · Cited by 53

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

Luu, N. T., Le, H.-M., Phan, Q.-H., & Pham, T.-T.-H. (2021) · Journal of Biomedical Optics · doi:10.1117/1.jbo.26.7.075001 · Cited by 51

A Systematic Review of Wearable Systems for Cancer Detection: Current State and Challenges

Ray, P. P., Dash, D., & De, D. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0828-y · Cited by 50

A Bayesian Sequential Learning Framework to Parameterise Continuum Models of Melanoma Invasion into Human Skin

Browning, A. P., Haridas, P., & Simpson, M. J. (2018) · Bulletin of Mathematical Biology · doi:10.1007/s11538-018-0532-1 · Cited by 49

Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

Qaisar, S. M., Khan, S. I., Dallet, D., Tadeusiewicz, R., & Pławiak, P. (2022) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2022.05.006 · Cited by 47

A Reinforcement Learning Algorithm for Automated Detection of Skin Lesions

Usmani, U. A., Watada, J., Jaafar, J., Aziz, I. A., & Roy, A. (2021) · Applied Sciences · doi:10.3390/app11209367 · Cited by 46

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks

Mazoure, B., Mazoure, A., Bédard, J., & Makarenkov, V. (2022) · Scientific Reports · doi:10.1038/s41598-021-03889-2 · Cited by 46

Computer Decision Support System for Skin Cancer Localization and Classification

Khan, M. A., Akram, T., Sharif, M., Kadry, S., & Nam, Y. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.016307 · Cited by 42

DermoCC-GAN: A new approach for standardizing dermatological images using generative adversarial networks

Salvi, M., Branciforti, F., Veronese, F., Zavattaro, E., Tarantino, V., Savoia, P., & Meiburger, K. M. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107040 · Cited by 41

Integration of modified ABCD features and support vector machine for skin lesion types classification

Melbin, K., & Raj, Y. J. V. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10056-8 · Cited by 40

Cancer diagnosis using artificial intelligence: a review

Shastry, K. A., & Sanjay, H. A. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10074-4 · Cited by 40

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning

Ghazal, T. M., Hussain, S., Khan, M. F., Khan, M. A., Said, R. A., & Ahmad, M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4826892 · Cited by 38

Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory

Elashiri, M. A., Arunachalam, R., Pandey, S. N., Shukla, S. K., Urooj, S., & Lay-Ekuakille, A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103729 · Cited by 38

Generation of a novel next-generation sequencing-based method for the isolation of new human papillomavirus types

Brancaccio, R. N., Robitaille, A., Dutta, S., Cuenin, C., Šantare, D., Šķenders, Ģ., Leja, M., Fischer, N., Giuliano, A. R., Rollison, D. E. M., et al. (2018) · Virology · doi:10.1016/j.virol.2018.04.017 · Cited by 36

An Early Intestinal Cancer Prediction Algorithm Based on Deep Belief Network

Wan, J.-J., Chen, B.-L., Kong, Y.-X., Ma, X., & Yu, Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-54031-2 · Cited by 35

Automatic segmentation and melanoma detection based on color and texture features in dermoscopic images

Oukil, S., Kasmi, R., Mokrani, K., & García-Zapirain, B. (2021) · Skin Research and Technology · doi:10.1111/srt.13111 · Cited by 35

Epileptic seizure classification using level-crossing EEG sampling and ensemble of sub-problems classifier

Hussain, S. F., & Qaisar, S. M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116356 · Cited by 34

An artificial neural network approach for detecting skin cancer

Sugiarti, S., Yunus, Y., Na’am, J., Indra, D., & Santony, J. (2019) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v17i2.9547 · Cited by 31

Web-based study on Chinese dermatologists’ attitudes towards artificial intelligence

Shen, C., Li, C., Xu, F., Wang, Z., Shen, X., Gao, J., Ko, R., Jing, Y., Tang, X., Yu, R., et al. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.12.102 · Cited by 31

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

López-Leyva, J. A., Guerra-Rosas, E., & Álvarez-Borrego, J. (2021) · IEEE Access · doi:10.1109/access.2021.3061873 · Cited by 29

Comparing an Artificial Neural Network to Logistic Regression for Predicting ED Visit Risk Among Patients With Cancer: A Population-Based Cohort Study

Sutradhar, R., & Barbera, L. C. (2020) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2020.02.010 · Cited by 28

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Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation

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Fitness adaptive deer hunting‐based region growing and recurrent neural network for melanoma skin cancer detection

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Current state of machine learning for non-melanoma skin cancer

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Detecting anomalous growth of skin lesion using threshold-based segmentation algorithm and Fuzzy K-Nearest Neighbor classifier

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An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine

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Social bat optimisation dependent deep stacked auto‐encoder for skin cancer detection

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Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

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A Survey work on Early Detection methods of Melanoma Skin Cancer

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Cost-Effectiveness of Sentinel Lymph Node Biopsy for Head and Neck Cutaneous Squamous Cell Carcinoma

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Artificial neural networks for simultaneously predicting the risk of multiple co‐occurring symptoms among patients with cancer

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Targeted next-generation sequencing of matched localized and metastatic primary high-risk SCCs identifies driver and co-occurring mutations and novel therapeutic targets.

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Premalignant lesions, basal cell carcinoma and melanoma in patients with cutaneous squamous cell carcinoma

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Accuracy of Deep Neural Network in Triaging Common Skin Diseases of Primary Care Attention

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Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Liao, J., Liu, L., Duan, H., Huang, Y., Zhou, L., Chen, L., & Wang, C. (2022) · JMIR Medical Informatics · doi:10.2196/28880 · Cited by 13

Multi-Class Skin Lesions Classification Using Deep Features

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Skin Cancer Classification using Random Forest

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Appliance Identification Based on Smart Meter Data and Event-Driven Processing in the 5G Framework

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Basal Cell Carcinoma on the Sole: An Easily Missed Cancer

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Personalized prediction of genes with tumor-causing somatic mutations based on multi-modal deep Boltzmann machine

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On the development of a skin cancer computer aided diagnosis system using support vector machine

Alamri, A., & AlSaeed, D. (2019) · Bioscience Biotechnology Research Communications · doi:10.21786/bbrc/12.2/12 · Cited by 8

Implementation of Support Vector Machine for Identification of Skin Cancer

Neela, A. G. (2019) · International Journal of Engineering and Manufacturing · doi:10.5815/ijem.2019.06.04 · Cited by 7

Experimental Comparative Study on Autoencoder Performance for Aided Melanoma Skin Disease Recognition

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The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas

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Dermoscopic Image Classification Using Deep Belief Learning Network Architecture

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Artificial Intelligence and Dermatologists: Friends or Foes?

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AUTOENCODER PERFORMANCE ANALYSIS OF SKIN LESION DETECTION

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Analysis of Skin Cancer using K-Means Clustering and Hybrid Classification Model

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A robust deep learning framework for multiclass skin cancer classification

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Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective

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A novel CNN-ViT-based deep learning model for early skin cancer diagnosis

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AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

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AI'S IMPACT ON PERSONALIZED MEDICINE: TAILORING TREATMENTS FOR IMPROVED HEALTH OUTCOMES

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Skin cancer detection using machine learning: Systematic review

Sharma, R., & Sandhu, D. A. (2026) · AIP conference proceedings · doi:10.1063/5.0327393

Revolutionizing Skin Cancer Diagnosis With Artificial Intelligence

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Machine Learning And Deep Learning Techniques For Automated Skin Cancer Detection: A Comprehensive Review

Chouhan, S., & Raghuwanshi, P. P. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.19630817

State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: a comprehensive review

Bhatt, H., Shah, V., Shah, K., Shah, R., & Shah, M. (2022) · Intelligent Medicine · doi:10.1016/j.imed.2022.08.004 · Cited by 138

Review on skin cancer detection using AI

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