Deep Learning in Dermatology: A Systematic Review of Image Analysis, Disease Classification, and Explainable AI
Keywords:
Clinical Decision Support, Deep Learning, Dermatology, Explainable Artificial Intelligence, Skin Lesion Classification, Vision TransformersAbstract
Deep learning is changing the way dermatology assessment is done through skin images, yet the evidence remains scattered across image preprocessing, segmentation, classification, explainability, and clinical validation. This systematic review analysed 40 studies published between 2017 and 2026. Google Scholar, IEEE Xplore, SpringerLink, and MDPI returned 52 records; after removing 12 duplicates, 40 unique studies met the inclusion criteria. Reporting quality was assessed with a five-point checklist. The evidence shows a move from manually selected features and classical CNNs to EfficientNet, attention mechanisms, vision transformers, and hybrid CNN-transformer architectures. ISIC, HAM10000, PH2, and PAD-UFES-20 are the main datasets, while Grad-CAM and its variants are gaining momentum for explaining model attention. Performance measures are difficult to compare because of differences in datasets, class divisions, validation techniques, and reported outcomes. Ongoing challenges include uneven class representation, restricted skin-tone variance, high computational requirements, uncertain calibration, weak external validation, and limited testing of the clinical relevance of explanations. Future work should prioritise diverse multicentre datasets, prospective workflow assessment, subgroup disclosure, lightweight deployment, and clinically valuable explanations.
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[1] Choy, S. P., Kim, B. J., Paolino, A., Tan, W. R., Lim, S. M. L., Seo, J., Tan, S. P., Francis, L., Tsakok, T., Simpson, M., Barker, J. N. W. N., Lynch, M. D., Corbett, M. S., Smith, C. H., & Mahil, S. K. (2023). Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease. npj Digital Medicine, 6, 180. https://doi.org/10.1038/s41746-023-00914-8
[2] Trakatelli, M., Richard, M.-A., Rouillard, A., Paul, C., Röcken, M., & Stratigos, A. (2023). The burden of skin disease in Europe. Journal of the European Academy of Dermatology and Venereology, 37(Suppl. 7), 3–5. https://doi.org/10.1111/jdv.19390
[3] Debelee, T. G. (2023). Skin lesion classification and detection using machine learning techniques: A systematic review. Diagnostics, 13(19), 3147. https://doi.org/10.3390/diagnostics13193147
[4] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
[5] Mazhar, T., Haq, I., Ditta, A., Mohsan, S. A. H., Rehman, F., Zafar, I., Gansau, J. A., & Goh, L. P. W. (2023). The role of machine learning and deep learning approaches for the detection of skin cancer. Healthcare, 11(3), 415. https://doi.org/10.3390/healthcare11030415
[6] Jeong, H. K., Park, C., Henao, R., & Kheterpal, M. (2023). Deep learning in dermatology: A systematic review of current approaches, outcomes, and limitations. JID Innovations, 3(1), 100150. https://doi.org/10.1016/j.xjidi.2022.100150
[7] Kawahara, J., & Hamarneh, G. (2019). Visual diagnosis of dermatological disorders: Human and machine performance [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1906.01256
[8] Liu, Y., Jain, A., Eng, C., Way, D. H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., Gupta, V., Singh, N., Natarajan, V., Hofmann-Wellenhof, R., Corrado, G. S., Peng, L. H., Webster, D. R., Ai, D., Huang, S. J., ... Coz, D. (2020). A deep learning system for differential diagnosis of skin diseases. Nature Medicine, 26(6), 900–908. https://doi.org/10.1038/s41591-020-0842-3
[9] Groh, M., Badri, O., Daneshjou, R., Koochek, A., Harris, C., Soenksen, L. R., Doraiswamy, P. M., & Picard, R. (2024). Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nature Medicine, 30(2), 573–583. https://doi.org/10.1038/s41591-023-02728-3
[10] Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., Paoli, J., Puig, S., Rosendahl, C., Soyer, H. P., Zalaudek, I., & Kittler, H. (2020). Human–computer collaboration for skin cancer recognition. Nature Medicine, 26(8), 1229–1234. https://doi.org/10.1038/s41591- 020-0942-0
[11] Ahammed, M., Mamun, M. A., & Uddin, M. S. (2022). A machine learning approach for skin disease detection and classification using image segmentation. Healthcare Analytics, 2, 100122. https://doi.org/10.1016/j.health.2022.100122
[12] Bandyopadhyay, S. K., Bose, P., Bhaumik, A., & Poddar, S. (2022). Machine learning and deep learning integration for skin diseases prediction. International Journal of Engineering Trends and Technology, 70(2), 11– 18. https://doi.org/10.14445/22315381/IJETT-V70I2P202
[13] Sun, J., Yao, K., Huang, G., Zhang, C., Leach, M., Huang, K., & Yang, X. (2023). Machine learning methods in skin disease recognition: A systematic review. Processes, 11(4), 1003. https://doi.org/10.3390/pr11041003
[14] Alahmadi, M. D., & Alghamdi, W. (2022). Semi-supervised skin lesion segmentation with coupling CNN and transformer features. IEEE Access, 10, 122560–122569. https://doi.org/10.1109/ACCESS.2022.3224005
[15] Fatima, S., Akram, M. U., Mohammad, S., & Ahmed, S. B. (2025). Deep learning in dermatopathology: Applications for skin disease diagnosis and classification. Discover Applied Sciences, 7(9), 1006. https://doi.org/10.1007/s42452-025-07138-3
[16] Khalaf, A. D., Hamdan, H., Abdul Halin, A., & Manshor, N. (2025). Segmentation and classification of skin cancer diseases based on deep learning: Challenges and future directions. IEEE Access, 13, 90163–90184. https://doi.org/10.1109/ACCESS.2025.3569170
[17] Yousaf, N., Amin, J., Butt, W. H., Zafar, A., & Kim, S. (2026). Advanced hybrid transformer–CNN framework for improved skin lesion classification and segmentation. Scientific Reports, 16, 13592. https://doi.org/10.1038/s41598-026-43376-0
[18] Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., & Halpern, A. (2018). Skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging, hosted by the International Skin Imaging Collaboration. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 168–172). IEEE. https://doi.org/10.1109/ISBI.2018.8363547
[19] Wen, D., Khan, S. M., Xu, A. J., Ibrahim, H., Smith, L., Caballero, J., Zepeda, L., de Blas Perez, C., Denniston, A. K., Liu, X., & Matin, R. N. (2022). Characteristics of publicly available skin cancer image datasets: A systematic review. The Lancet Digital Health, 4(1), e64–e74. https://doi.org/10.1016/S2589-7500(21)00252-1
[20] Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Langer, S., Liopyris, K., Malvehy, J., Musthaq, S., Nanda, J., ... Soyer, H. P. (2021). A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Scientific Data, 8, 34. https://doi.org/10.1038/s41597-021-00815-z
[21] Groh, M., Harris, C., Soenksen, L. R., Lau, F., Han, R., Kim, A., Koochek, A., & Badri, O. (2021). Evaluating deep neural networks trained on clinical images in dermatology with the Fitzpatrick 17k dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 1820–1828). IEEE. https://doi.org/10.1109/CVPRW53098.2021.00201
[22] Mendonça, T., Ferreira, P. M., Marques, J. S., Marçal, A. R. S., & Rozeira, J. (2013). PH2–A dermoscopic image database for research and benchmarking. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5437–5440). IEEE. https://doi.org/10.1109/EMBC.2013.6610779
[23] Pacheco, A. G. C., Lima, G. R., Salomão, A. S., Krohling, B., Biral, I. P., de Angelo, G. G., Alves, F. C. R., Jr., Esgario, J. G. M., Simora, A. C., Castro, P. B. C., Rodrigues, F. B., Frasson, P. H. L., Krohling, R. A., Knidel, H., Santos, M. C. S., do Espírito Santo, R. B., Macedo, T. L. S. G., Canuto, T. R. P., & de Barros, L. F. S. (2020). PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Data in Brief, 32, 106221. https://doi.org/10.1016/j.dib.2020.106221
[24] Tschandl, P., Rosendahl, C., & Kittler, H. (2018). The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5, 180161. https://doi.org/10.1038/sdata.2018.161
[25] Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., Mukherjee, P., Phung, M., Yekrang, K., Fong, B., Sahasrabudhe, R., Allerup, J. A. C., Okata-Karigane, U., Zou, J., & Chiou, A. S. (2022). Disparities in dermatology AI performance on a diverse, curated clinical image set. Science Advances, 8(32), eabq6147. https://doi.org/10.1126/sciadv.abq6147
[26] Groh, M., Harris, C., Daneshjou, R., Badri, O., & Koochek, A. (2022). Towards transparency in dermatology image datasets with skin tone annotations by experts, crowds, and an algorithm [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2207.02942
[27] Kshirsagar, P. R., Manoharan, H., Shitharth, S., Alshareef, A. M., Albishry, N., & Balachandran, P. K. (2022). Deep learning approaches for prognosis of automated skin disease. Life, 12(3), 426. https://doi.org/10.3390/life12030426
[28] Yuan, C., Zhao, D., & Agaian, S. S. (2024). UCM-Net: A lightweight and efficient solution for skin lesion segmentation using MLP and CNN. Biomedical Signal Processing and Control, 96, 106573. https://doi.org/10.1016/j.bspc.2024.106573
[29] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
[30] Haner Kırğıl, E. N., & Erdaş, Ç. B. (2024). Enhancing skin disease diagnosis through deep learning: A comprehensive study on dermoscopic image preprocessing and classification. International Journal of Imaging Systems and Technology, 34(4), e23148. https://doi.org/10.1002/ima.23148
[31] Al-Masni, M. A., Kim, D.-H., & Kim, T.-S. (2020). Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Computer Methods and Programs in Biomedicine, 190, 105351. https://doi.org/10.1016/j.cmpb.2020.105351
[32] Anjum, M. A., Amin, J., Sharif, M., Khan, H. U., Malik, M. S. A., & Kadry, S. (2020). Deep semantic segmentation and multi-class skin lesion classification based on convolutional neural network. IEEE Access, 8, 129668–129678. https://doi.org/10.1109/ACCESS.2020.3009276
[33] Basak, H., Kundu, R., & Sarkar, R. (2022). MFSNet: A multi-focus segmentation network for skin lesion segmentation. Pattern Recognition, 128, 108673. https://doi.org/10.1016/j.patcog.2022.108673
[34] Bi, L., Kim, J., Ahn, E., Kumar, A., Fulham, M., & Feng, D. (2017). Dermoscopic image segmentation via multistage fully convolutional networks. IEEE Transactions on Biomedical Engineering, 64(9), 2065–2074. https://doi.org/10.1109/TBME.2017.2712771
[35] Goyal, M., Oakley, A., Bansal, P., Dancey, D., & Yap, M. H. (2020). Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access, 8, 4171–4181. https://doi.org/10.1109/ACCESS.2019.2960504
[36] Nida, N., Irtaza, A., Javed, A., Yousaf, M. H., & Mahmood, M. T. (2019). Melanoma lesion detection and segmentation using deep region-based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics, 124, 37–48. https://doi.org/10.1016/j.ijmedinf.2019.01.005
[37] Liu, H., Dou, Y., Wang, K., Zou, Y., Sen, G., Liu, X., & Li, H. (2025). A skin disease classification model based on multi-scale combined efficient channel attention module. Scientific Reports, 15, 6116. https://doi.org/10.1038/s41598-025-90418-0
[38] Malik, S. G., Jamil, S. S., Aziz, A., Ullah, S., Ullah, I., & Abohashrh, M. (2024). High-precision skin disease diagnosis through deep learning on dermoscopic images. Bioengineering, 11(9), 867. https://doi.org/10.3390/bioengineering11090867
[39] Ravi, V. (2022). Attention cost-sensitive deep learning-based approach for skin cancer detection and classification. Cancers, 14(23), 5872. https://doi.org/10.3390/cancers14235872
[40] Hammad, M., Pławiak, P., ElAffendi, M., El-Latif, A. A. A., & Latif, A. A. A. (2023). Enhanced deep learning approach for accurate eczema and psoriasis skin detection. Sensors, 23(16), 7295. https://doi.org/10.3390/s23167295
[41] Ali, S. N., Ahmed, M. T., Paul, J., Jahan, T., Sani, S. M. S., Noor, N., & Hasan, T. (2022). Monkeypox skin lesion detection using deep learning models: A feasibility study [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2207.03342
[42] Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020). Grad-CAM: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128(2), 336–359. https://doi.org/10.1007/s11263-019-01228-7
[43] Alenezi, F., Armghan, A., & Polat, K. (2023). A novel multi-task learning network based on melanoma segmentation and classification with skin lesion images. Diagnostics, 13(2), 262. https://doi.org/10.3390/diagnostics13020262
[44] Bordoloi, D., Singh, V., Kaliyaperumal, K., Ritonga, M., Jawarneh, M., Kassanuk, T., & Quiñonez-Choquecota, J. (2023). Classification and detection of skin disease based on machine learning and image processing evolutionary models. Computer Assisted Methods in Engineering and Science, 30(2), 247–256. https://doi.org/10.24423/CAMES.479
[45] Rangaswamy, S., Tantry, S. S., & Lal, T. S. (2025). Skin disease classification using deep learning. National Academy Science Letters, 48(5), 585–588. https://doi.org/10.1007/s40009-024-01523-z
[46] Sarı, M. O., & Keser, K. (2025). Classification of skin diseases with deep learning-based approaches. Scientific Reports, 15, 27506. https://doi.org/10.1038/s41598-025-13275-x
[47] Singh, S., & Pandey, J. K. (2026). Hybrid deep feature fusion for facial emotion recognition using VGG19 and ResNet152V2. International Journal of Innovative Research in Computer Science and Technology, 14(3), 17-26. https://doi.org/10.55524/ijircst.2026.14.2.10
[48] Pandey, J. K., Verma, S. K., Kumar, J., Perwej, Y., Jha, S. K., Panchal, B. Y., Ferdouse, R., Sindhu, V., Banerjee, S., Tiwari, M., Badal, R., Mandal, P., & Baghel, J. S. (2026). Transformative role of advanced neural computation in clinical image diagnostics: A review of key concepts and applications. Seminars in Ultrasound, CT and MRI. Advance online publication. https://doi.org/10.1053/j.sult.2026.06.010
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