DermoAI-CNN: Leveraging GANs, Mask R-CNN, and Attention Mechanisms for Enhanced Skin Disease Analysis

• Autorzy: Prasad Rajesh , Saxena Kushagr , Maurya Ayush , Prajapati Yogendra Narayan

Identyfikatory

DOI

10.2478/fcds-2025-0021

• Języki publikacji: EN

Abstrakty

EN

The study aims to develop an effective and efficient deep learning model for detecting skin diseases, as skin diseases rank as the world's number one health problem. Besides, cancers and dermatological anomalies should be diagnosed at an early stage, so that subsequent treatment can be efficient and complication-free. The existing methods of diagnosis are associated with lower precision and, in most cases, are inefficient, which can be attributed to the lack of effective data augmentation, segmentation techniques, and improved feature extraction. In this paper, a general framework is introduced that uses Generative Adversarial Networks for data augmentation, Mask R-CNN for precise segmentation, and a tailored multilayer Convolutional Neural Network with an attention mechanism incorporated into it to classify 23 skin disease classes using 25,250 images, among them 5,750 generated by GAN, to balance underrepresented classes. The accuracy attained was 97.30%, which was much better than that reported in earlier studies, which ranged from 85 to 92. The metrics, including an accuracy of 95.65%, a recall of 97.09%, and an F1-score of 96.98%, were used to assess the system's performance in classifying invisible dermatological images. The scalable system provides explanations that support real-time diagnosis, preventing delays and acute health costs. The findings fully fulfil the capabilities of deep learning in dermatology, as the initial diagnosis of the skin disease is accurate, accessible and efficient.

Słowa kluczowe

EN

skin disease detection; Convolutional Neural Network; image processing; data augmentation; early detection; diseases classification; accuracy; automation in HealthCare

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2025
• Tom: Vol. 50, No. 4
• Strony: 537--569
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Prasad Rajesh

• Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India

autor

Saxena Kushagr

• Ajay Kumar Garg Engineering College, Ghaziabad, India

autor

Maurya Ayush

• Infosys Limited, Bangalore, India

autor

Prajapati Yogendra Narayan

• Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India

Bibliografia

• 1. A Razia. S., Chamola, V., Hussain, Z., Albalwy, F., & Hussain, A, A novel end-to-end deep convolutional neural network based skin lesion classification framework, Expert Systems with Applications, 246. 2024, 1-19.
• 2. Ali, R., Manikandan, A., & Xu, J, A Novel framework of Adaptive fuzzy-GLCM Segmentation and Fuzzy with Capsules Network (F-CapsNet) Classification, Neural Computing and Applications, 35, 2023, 22133-22149.
• 3. Attallah, O., Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning, Computers in Biology and Medicine, 178, 108798, 2024.
• 4. Bagheri, F., Tarokh, M. J., & Ziaratban, M, Skin lesion segmentation from dermoscopic images by using Mask R-CNN, Retina-Deeplab, and graph-based methods, Biomedical Signal Processing and Control, 67. 2021, 102533.
• 5. Burada, S., Manjunathswamy, B. E., & Sunil Kumar, M, Early detection of melanoma skin cancer: A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional neural network, Measurement: Sensors, 33, 2024, 101168, 1-9.
• 6. DermNet N. Z. Dermnet. https://www.kaggle.com/datasets/shubhamgoel27/dermnet, 2024
• 7. Fan, S. K. S., & Chen, W. Y., A generative-adversarial-network-based temporal raw trace data augmentation framework for fault detection in semiconductor manufacturing, Engineering Applications of Artificial Intelligence, 139(part B), 2025, 109624.
• 8. Farooq, M. A., Khatoon, A., Varkarakis, V., & Corcoran, P., Advanced deep learning methodologies for skin cancer classification in prodromal stages, CEUR Workshop Proceedings, 2019, 2563.
• 9. 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 (Switzerland), 10(7). 2022, 1183, 1-18.
• 10. Gurunathan, A., & Krishnan, B., A Hybrid CNN-GLCM Classifier For Detection And Grade Classification of Brain Tumor, Brain Imaging and Behavior, 16(3). 2022, 1410-1427.
• 11. Jeong, H. K., Park, C., Henao, R., & Kheterpal, M., Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations, JID Innovations, 3(1), 2023, 100150, 1-16.
• 12. Kao, S. Y. Z., Ekwueme, D. U., Holman, D. M., Rim, S. H., Thomas, C. C., & Saraiya, M., Economic burden of skin cancer treatment in the USA: an analysis of the Medical Expenditure Panel Survey Data, 2012–2018, Cancer Causes and Control, 34(3), 2023, 205-212.
• 13. Kumar, K. S., Suganthi, N., Muppidi, S., & Kumar, B. S., FSPBO-DQN: SeGAN based segmentation and Fractional Student Psychology Optimization enabled Deep Q Network for skin cancer detection in IoT applications, Artificial Intelligence in Medicine, 129, 2022, 102299.
• 14. McHale, C. M., Osborne, G., Morello-Frosch, R., Salmon, A. G., Sandy, M. S., Solomon, G., Zhang, L., Smith, M. T., & Zeise, L., Assessing health risks from multiple environmental stressors: Moving from G × E to I × E, Mutation Research - Reviews in Mutation Research, 775, 2018, 11-20.
• 15. Mohan, J., Sivasubramanian, A., V., S., & Ravi, V., Enhancing skin disease classification leveraging transformer-based deep learning architectures and explainable AI, Computers in Biology and Medicine, 190, 2025, 110007.
• 16. Oztel, I., Yolcu Oztel, G., & Sahin, V. H., Deep Learning-Based Skin Diseases Classification using Smartphones, Advanced Intelligent Systems, 5(12), 2023, 2300211, 1-11.
• 17. Ravi, V., Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification, Cancers, 14(23), 2022, 1-26.
• 18. Rodrigues, D. de A., Ivo, R. F., Satapathy, S. C., Wang, S., Hemanth, J., & Filho, P. P. R., A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system, Pattern Recognition Letters, 136, 2020, 8-15.
• 19. Saha, D. K., Joy, A. M., & Majumder, A., YoTransViT: A transformer and CNN method for predicting and classifying skin diseases using segmentation techniques, Informatics in Medicine Unlocked, 47, 101495, 2024, 1-15.
• 20. Schmitges, F. W., Radovani, E., Najafabadi, H. S., Barazandeh, M., Campitelli, L. F., Yin, Y., Jolma, A., Zhong, G., Guo, H., Kanagalingam, T., Dai, W. F., Taipale, J., Emili, A., Greenblatt, J. F., Hughes, T. R., Zhou, X., Weeks, S. D., Ameloot, P., Callewaert, N., … Zammit, P. P. S. Melanoma: Risk factors, early detection, and treatment strategies-An updated review. Skeletal Muscle, 6(1), 2016.
• 21. Serte, S., & Demirel, H., Gabor wavelet-based deep learning for skin lesion classification, Computers in Biology and Medicine, 113, 103423, 2019.
• 22. Shukla M. M. et al., A hybrid CNN with transfer learning for skin cancer disease detection, Medical & Biological Engineering & Computing, 62(10), 2024, 3057-3071.
• 23. 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, 21(8), 2021, 1-27.
• 24. Tschandl, P., Rosendahl, C., & Kittler, H., Data descriptor: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Scientific Data, 5, 2018, 180161.
• 25. Urban, K., Mehrmal, S., Uppal, P., Giesey, R. L., & Delost, G. R., The global burden of skin cancer: A longitudinal analysis from the Global Burden of Disease Study, 1990–2017, JAAD International, 2, 2021, 98-108.
• 26. Varma, P. B. S., Paturu, S., Mishra, S., Rao, B. S., Kumar, P. M., & Krishna, N. V., SLDCNet: Skin lesion detection and classification using full resolution convolutional network-based deep learning CNN with transfer learning, Expert Systems, 39(9), 2022.
• 27. Zareen, S. S., Sun, G., Kundi, M., Qadri, S. F., & Qadri, S., Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach, Computers, Materials and Continua, 79(1), 2024, 1497-1519.
• 28. Zenghong Wu, Fangnan Xia, R. L., Global burden of cancer and associated risk factors in 204 countries and territories, 1980-2021: a systematic analysis for the GBD 2021, J Hematol Oncol, 17(1: 119), 2024, 1-14.
• 29. Zhang, B., Zhou, X., Luo, Y., Zhang, H., Yang, H., Ma, J., & Ma, L., Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning, Chinese Journal of Mechanical Engineering (English Edition), 34(1), 2021.
• 30. Zhang, J., Zhong, F., He, K., Ji, M., Li, S., & Li, C., Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review, Diagnostics, 13(23), 2023, 1-30.