Deep Learning-Based Approach for Detecting Multiple Skin Diseases: A Comprehensive Study

Presents a deep learning-based approach for classifying five types of skin diseases: Vitiligo, Rosacea, Impetigo, Ringworm, and Hives. A transfer learning method was employed using MobileNetV3 (Small & Large), InceptionV3, Xception, DenseNet201, and DenseNet121. To enhance model performance, the dataset underwent preprocessing (image resizing, normalization) and augmentation (rotation, flipping, zooming, and brightness adjustment). Experimental results show that DenseNet121 achieved the highest accuracy of 94.40% with a test loss of 0.63, demonstrating its effectiveness in skin disease classification. This study highlights the potential of transfer learning for automated dermatological diagnosis and suggests future improvements, including expanding datasets with diverse skin tones and mobile-based implementations.