Classification of Skin Diseases Using Transfer Learning with ResNet-50 Architecture and Data Preprocessing Using Real-ESRGAN and Wiener Filter - Jiemesha, Micheila; Wonohadidjojo, Daniel Martomanggolo (Intelligent System and Computation, 2025)
The skin is a vital organ which serves as a barrier against external factors, yet it’s highly susceptible to diseases. These diseases are often presented as lesions with similar appearances, making it difficult to be diagnosed and prone to human errors. To address this challenge, this study uses Deep Learning, particularly the ResNet-50 architecture using Transfer Learning, to classify skin diseases from lesion. In this study, data augmentation is implemented to increase dataset size, thus improving model performance and preventing overfitting. Data is then preprocessed using Real-ESRGAN to enhance resolution and the Wiener Filter to sharpen the features. Adam optimizer is used to further enhance the model’s performance. Hyperparameter tuning is also implemented to optimize the model parameters, and dropout regularization is applied to enhance the model's ability to be able to accurately classify unseen data. The model managed to achieve a high accuracy of 99.09%, with 0.96 precision, 0.95 recall, and 0.95 F1-score. These results demonstrate the effectiveness of combining Real-ESRGAN and Wiener Filter with the ResNet-50 architecture and the Adam optimizer in developing a robust model for skin disease classification. This approach offers a promising tool for healthcare professionals which may help reduce human error in dermatological diagnosis.