CNN and Transfer Learning methods for enhanced dermatological disease detection

• Autorzy: Venkataiah Habibulla C. , Jayachandra Prasad T. , Gopinath G. , Charitha B. , Dharma Teja G. , Lomith Reddy B.

Identyfikatory

DOI

10.24425/ijet.2025.153607

• Języki publikacji: EN

Abstrakty

EN

Since skin diseases generally badly affect lives, the earlier and more accurate the diagnosis, the better the chances of effective treatment and a better prognosis. Deep learning applications, especially CNNs, has revolutionized the domain of disease classification, significantly increasing the accuracy of diagnoses for such common conditions and facilitating early interventions. The huge success behind the ongoing project motivated advancements of the developing in CNN techniques towards detection of skin disease by using the concept of Transfer Learning. So, the older models, which had employed it for detecting Eczema and Psoriasis based on the architectures involving deep CNNs. The Inception ResNet v2 architecture improved the accuracy of that model, with some practical implementations via smartphone integration and web server integration. Some of those innovations are as follows in our project. The earlier work used different CNN architectures. Our approach involved Transfer Learning with a pre-trained ResNet50 model to try to improve performance and efficiency using features learned from large-scale datasets. This reduce the complexity and enhance the accuracy. Besides Transfer Learning adaptation, our project encompasses elaborate preprocessing techniques like resizing, normalization, and data augmentation in fine-tuning the dataset for further model fine-tuning. It has 97.6% accuracy, 95% precision, 99.4% recall, and 97.4% F1-score. rad-CAM techniques have been employed to visualize and interpret model predictions. This final model has been a pragmatic and accessible tool for early detection and diagnosis of skin disease. The feature here is an attempt to provide a more accurate, efficient, and user-friendly diagnostic solution through the incorporation of advanced methods of Transfer Learnin3g and visualization.

Słowa kluczowe

EN

eczema; psoriasis; dermatology; CNN; transfer learning

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 2
• Strony: 337--344
• Opis fizyczny: Bibliogr. 16 poz., tab., rys., fot.

Twórcy

autor

Venkataiah Habibulla C.

• College of Engineering and Technology, Nandyal, India

autor

Jayachandra Prasad T.

• College of Engineering and Technology, Nandyal, India

autor

Gopinath G.

• College of Engineering and Technology, Nandyal, India

autor

Charitha B.

• College of Engineering and Technology, Nandyal, India

autor

Dharma Teja G.

• College of Engineering and Technology, Nandyal, India

autor

Lomith Reddy B.

• College of Engineering and Technology, Nandyal, India

Bibliografia

• [1] Md. Sazzadul Islam Prottasha ,SanjanMahjabinFarin, Md. Bulbul Ahmed, Md. Zihadur Rahman, A. B. M. Kabir Hossain, and M. Shamim Kaiser under exclusive license to Springer Nature Singapore Pte Ltd. A Deep Learning-Based Skin Disease Detection Using Convolutional Neural Networks (CNN). 2023 https://link.springer.com/chapter/10.1007/978-981-19-8032-9_39
• [2] Data set link: https://www.kaggle.com/datasets/gopinath12316/skindiseasedatase1/settings
• [3] Al Abbadi, N.K., Dahir, N.S., AL-Dhalimi, M.A., Restom, H.: Psoriasis detection using skin color and texture features. J. Comput. Sci. 6(6), 648-652 (2010).
• [4] Source code link for some part of the method: https://www.kaggle.com/code/gopinath12316/resnet50-with-2
• [5] Alam,M.N.,Munia, T.T.K., Tavakolian, K., Vasefi, F., MacKinnon, N., Fazel-Rezai, R., “Automatic detection and severity measurement of eczema using image processing” in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1365-1368. IEEE (2016).
• [6] S. Taj, G. M. Shaikh, S. Hassan, "Urdu Speech Emotion Recognition using Speech Spectral Features and Deep Learning Techniques", Proceedings of the 4th International Conference on Computing, Mathematics and Engineering Technologies, Sukkur, Pakistan, 17-18 March 2023, pp. 1-6.
• [7] M. Hamidi, F. Barkani, O. Zealouk, H. Satori, "Assessing the Performance of a Speech Recognition System Embedded in Low-Cost Devices", International Journal of Electrical and Computer Engineering Systems, Vol. 14, No. 6, 2023, pp. 677-683.
• [8] S. K. Nayak, A. K. Nayak, S. Mishra, P. Mohanty, N. Tripathy, S. Prusty, An Indonesian Journal of Electrical Engineering and Computer Science named “Improving Kui digit recognition through machine learning and data augmentation techniques”, , Vol. 35, No. 2, 2024, pp. 867-877
• [9] Bhadula, S., Sharma, S., Juyal, P., Kulshrestha, C.: Machine learning algorithms-based skin disease detection. IJITEE 9(2), 4044-4049 (2019).
• [10] Shanthi T., Sabeenian R.S., Anand R, Automatic diagnosis of skin diseases using convolution neural network. Microprocess. Microsyst. 76, 103074 (2020)
• [11] Shankar, V., Kumar, V., Devagade, U., Karanth, V., Rohitaksha, K.: Heart disease prediction using CNN algorithm. SN Comput. Sci. 1, 170 (2020)
• [12] Prottasha, M.S.I., Hossain, A.B.M., Rahman, M.Z., Reza, S.M.S., Hossain, D.A.: Identification of various rice plant diseases using optimized convolutional neural network. Int. J. Comput. Dig. Syst. (IJCDS) 9 (2021)
• [13] Farooq, A., Anwar, S., Awais, M., Rehman, S.: A deep CNN based multi-class classification of Alzheimer’s disease using MRI. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST). Dermnet: Dermatology Skin Disease Pictures. http://www.dermnet.com/images/ Eczema-Photos. Last accessed 30 Aug. 2019
• [14] Psoriasis Department of Dermatology. https://medicine.uiowa.edu/dermatology/psoriasis. Last accessed 27 Aug. 2019
• [15] K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition”. CoRR. abs/1409.1556 (2015) 564 Md. Sazzadul Islam Prottasha et al.
• [16] E. Goceri, Diagnosis of skin diseases in the era of deep learning and mobile technology. Comput. Biol. Med.2021,134,104458. https://doi.org/10.1016/j.compbiomed.2021.104458

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).