SKIN DISEASE PREDICTION BASED ON MEDICAL IMAGES USING DEEP LEARNING TECHNIQUES: A REVIEW
DOI:
https://doi.org/10.63503/c.acset.2025.8Keywords:
Deep learning, Convolutional Neural Networks, Benign, Malignant, Skin cancer (skin disease)Abstract
Skin diseases are common these days. Ignoring these diseases can lead to development of skin cancer in the current situation. In recent years, deep learning has evolved as an emerging technique for predicting disease more efficiently in the early stages as compared to other techniques. This greatly benefits from the application of image examination technique which is broadly beneficial to Medical Services. In Dermatology’s intricacy and high cost make it challenging to diagnose. Using deep learning dermatoscopic images can be detected at an early stage. Early-stage detection of skin lesions is useful to predict whether the lesion can lead to cancer and then it can be treated before it spreads throughout the different organs of body. The growing incidence of skin conditions worldwide calls for the creation of automated, scalable, and precise diagnostic tools to support primary care physicians and dermatologists.This paper provides a survey on some of the prevalent skin diseases focusing on their symptoms, prediction and accuracy via focusing on recent advancement of using deep learning techniques.The purpose of this study is to provide researchers and clinicians a collective understanding of how deep learning methods can improve diagnostic accuracy, reduce manual workload, and support clinical decision-making in dermatology.
References
1. Yuan, Y. Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. arXiv preprint, 2017. arXiv:1703.05165. https://doi.org/10.48550/arXiv.1703.05165
2. Patnaik, S.K., Sidhu, M.S., Gehlot, Y., Sharma, B., & Muthu, P. Automated skin disease identification using deep learning algorithm. Biomedical & Pharmacology Journal, 2018. 11(3): p.1429.
3. Mendes, D.B., & da Silva, N.C. Skin lesions classification using convolutional neural networks in clinical images. arXiv e-prints, 2018. arXiv:1812.02316. https://doi.org/10.48550/arXiv.1812.02316
4. Pal, A., Ray, S., & Garain, U. Skin disease identification from dermoscopy images using deep convolutional neural network. 2018. https://challenge2018.isic-archive.com/task3/
5. Cai, D., Ardakany, A.R., & Ay, F. Deep learning-aided diagnosis of autoimmune blistering diseases. medRxiv, 2021. https://doi.org/10.1101/2021.11.27.21266845
6. UzunOzsahin, D., Mustapha, M.T., Uzun, B., Duwa, B., & Ozsahin, I. Computer-aided detection and classification of monkeypox and chickenpox lesion in human subjects using deep learning framework. Diagnostics, 2023. 13(2): p.292. https://doi.org/10.3390/diagnostics13020292
7. Gouda, W., Sama, N.U., Al-Waakid, G., Humayun, M., & Jhanjhi, N.Z. Detection of skin cancer based on skin lesion images using deep learning. Healthcare, 2022. 10(7): p.1183. https://doi.org/10.3390/healthcare10071183
8. Ghosh, P., Azam, S., Quadir, R., Karim, A., Shamrat, F.M.J.M., Bhowmik, S.K., Jonkman, M., Hasib, K.M., & Ahmed, K. SkinNet-16: A deep learning approach to identify benign and malignant skin lesions. Frontiers in Oncology, 2022. 12:931141. https://doi.org/10.3389/fonc.2022.931141
9. Harangi, B. Skin lesion classification with ensembles of deep convolutional neural networks. Journal of Biomedical Informatics, 2018. 86: p.25–32. https://doi.org/10.1016/j.jbi.2018.08.006
10. Kadampur, M.A., & Riyaee, S.A. Skin cancer detection: Applying a deep learning-based model-driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked, 2019. 18:100282. https://doi.org/10.1016/j.imu.2019.100282
11. Srinivasu, P.N., SivaSai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., & Kang, J.J. Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 2021. 21:2852. https://doi.org/10.3390/s21082852
12. Prodeep, A.R., Araf, R., Ray, P., Ulubbi, M.S.A., Ananna, S.N., & Mridha, M.F. Acne and rosacea detection from images using deep CNN’s EfficientNet. In 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). 2022. IEEE. https://doi.org/10.1109/accai53970.2022.9752534
13. Lee, Y.C., Jung, S., & Won, H. WonDerM: Skin lesion classification with fine-tuned neural networks. arXiv preprint, 2018. arXiv:1808.03426. https://doi.org/10.48550/arXiv.1808.03426
14. Gessert, N., Nielsen, M., Shaikh, M., Werner, R., & Schlaefer, A. Skin lesion classification using ensembles of multi-resolution EfficientNets with metadata. arXiv preprint, 2019. arXiv:1910.03910. https://doi.org/10.48550/arXiv.1910.03910
15. Ameri, A. A deep learning approach to skin cancer detection in dermoscopy images. Journal of Biomedical Physics and Engineering, 2020. 10(6). https://doi.org/10.31661/jbpe.v0i0.2004-1107
16. Nadipineni, H. Method to classify skin lesions using dermoscopic images. arXiv preprint, 2020. arXiv:2008.09418. https://doi.org/10.48550/arxiv.2008.09418
17. Chaturvedi, S.S., Tembhurne, J.V., & Diwan, T. A multi-class skin cancer classification using deep convolutional neural networks. Multimedia Tools and Applications, 2020. 79: p.28477–28498.
18. Raza, R., Zulfiqar, F., Tariq, S., Anwar, G.B., Sargano, A.B., & Habib, Z. Melanoma classification from dermoscopy images using ensemble of convolutional neural networks. Mathematics, 2021. 10(1):26. https://doi.org/10.3390/math10010026
19. Sitaula, C., & Shahi, T.B. Monkeypox virus detection using pre-trained deep learning-based approaches. Journal of Medical Systems, 2022. 46(11): p.78. https://doi.org/10.1007/s10916-022-01868-2
20. Lan, Z., Cai, S., He, X., & Wen, X. FixCaps: An improved capsules network for diagnosis of skin cancer. IEEE Access, 2022. 10: p.76261–76267.
21. Kshirsagar, P.R., Manoharan, H., Shitharth, S., Alshareef, A.M., Albishry, N., & Balachandran, P.K. Deep learning approaches for prognosis of automated skin disease. Life, 2022. 12:426. https://doi.org/10.3390/life12030426
22. Basak, H., Kundu, R., & Sarkar, R. MFSNet: A multi-focus segmentation network for skin lesion segmentation. Pattern Recognition, 2022. 128:108673. https://doi.org/10.1016/j.patcog.2022.108673
23. Saifan, R., & Jubair, F. Six skin diseases classification using deep convolutional neural network. International Journal of Electrical and Computer Engineering, 2022. 12(3): p.3072–3082. https://doi.org/10.11591/ijece.v12i3.pp3072-3082
24. Ariansyah, M.H., Winarno, S., & Sani, R.R. Monkeypox and measles detection using CNN with VGG-16 transfer learning. Journal of Computing Research and Innovation, 2023. 8(1): p.32–44. https://doi.org/10.24191/jcrinn.v8i1.340
25. Wei, M., Wu, Q., Ji, H., Wang, J., Lyu, T., Liu, J., & Zhao, L. A skin disease classification model based on DenseNet and ConvNeXt fusion. Electronics, 2023. 12:438. https://doi.org/10.3390/electronics12020438
26. Gautam, V., Trivedi, N.K., Anand, A., Tiwari, R., Zaguia, A., & Koundal, D. Early skin disease identification using deep neural network. Computer Systems Science and Engineering, 2023. 44(3): p.2259–2275. http://www.techscience.com/csse/v44n3/49136
27. Jaradat, A.S., Al Mamlook, R.E., Almakayeel, N., Alharbe, N., Almuflih, A.S., Nasayreh, A., Gharaibeh, H., Gharaibeh, A., & Bzizi, H. Automated monkeypox skin lesion detection using deep learning and transfer learning techniques. International Journal of Environmental Research and Public Health, 2023. 20:4422. https://doi.org/10.3390/ijerph20054422
28. Mahum, R., & Aladhadh, S. Skin lesion detection using hand-crafted and DL-based features fusion and LSTM. Diagnostics, 2022. 12:2974. https://doi.org/10.3390/diagnostics12122974
29. Al Enezi, N.S.A. A model for classification of skin disease using image processing techniques and neural network. In International Learning & Technology Conference (ILTC), 2017. Jeddah, Saudi Arabia. pp.106–112.
30. Iquran, H., Qasmieh, I.A., Alqudah, A.M., Alhammouri, S., & Alawneh, E. The melanoma skin cancer detection and classification using support vector machine. In IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2017. Aqaba, Jordan: IEEE. pp.1–5.
31. Zhang, X., Wang, S., Liu, J., & Tao, C. Computer-aided diagnosis of four common cutaneous diseases using deep learning algorithm. In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017. Kansas City, MO, USA: IEEE. pp.1304–1306.
32. Asghar, M.Z., Asghar, M.J., Saqib, S.M., Ahmad, B., & Ahmad, S. Diagnosis of skin diseases using online expert system. International Journal of Computer Science and Information Security, 2011. 9(6): p.323–325.
33. Ali, N.M., Malpani, T., Kaundal, T., & Soni, D. Disease prediction web app using machine learning. International Research Journal of Modern Engineering and Technology Science, 2022.
34.Kaggle Dataset: Skin Cancer MNIST – HAM10000. https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000
35. Kaggle Dataset: Skin Cancer 9 Classes – ISIC. https://www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic
36. Yasir, R., Rahman, M.A., & Ahmed, N. Dermatological disease detection using image processing and artificial neural network. International Journal for Research in Applied Science & Engineering Technology, 2024. 8(9): p.1074–1079.
37. Mahmoud, N.M., & Soliman, A.M. Early automated detection system for skin cancer diagnosis using artificial intelligent techniques. Scientific Reports, 2024. 14:9749. https://doi.org/10.1038/s41598-024-59783-0
38. Yang, A., & Yang, E. A multimodal approach to the detection and classification of skin diseases. arXiv preprint, 2024. arXiv:2411.00498.
39. Behara, K., Bhero, E., & Agee, J.T. An improved skin lesion classification using a hybrid approach with active contour snake model and lightweight attention-guided capsule networks. Diagnostics, 2024. 14(6):636. https://doi.org/10.3390/diagnostics14060636
40. Malik, S.G., Jamil, S.S., Aziz, A., Ullah, S., Ullah, I., & Abohashrh, M. High-precision skin disease diagnosis through deep learning on dermoscopic images. Bioengineering, 2024. 11(9):867. https://doi.org/10.3390/bioengineering11090867
41. Aquil, A., Saeed, F., Baowidan, S., Ali, A.M., & Elmitwally, N.S. Early detection of skin diseases across diverse skin tones using hybrid machine learning and deep learning models. Information, 2025. 16(2):152. https://doi.org/10.3390/info16020152
42. Mahbod, A., Schaefer, G., Wang, C., Ecker, R., & Ellinge, I. Skin lesion classification using hybrid deep neural networks. In ICASSP 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019. Brighton, UK: IEEE. pp.1229–1233.
