Advance Transfer Learning Approach for Identification of Multiclass Skin Disease with LIME Explainable AI Technique

In dermatological diagnosis, accurately and appropriately classifying skin diseases is crucial for timely treatment, thereby improving patient outcomes. Our goal is to develop transfer learning models that can detect skin disease from images. We performed our study in the "Skin Cancer: MNIST HAM10000" dataset. This dataset has seven categories, including melanocytic nevi, melanoma, benign keratosis (solar lentigo/seborrheic keratosis), basal cell carcinoma, actinic keratoses, intraepithelial carcinoma (Bowen’s disease), vascular lesions, and more. To leverage pre-trained feature extraction, we use five available models—ResNet50, InceptionV3, VGG16, VGG19, and MobileNetV2. Overall results from these models show that ResNet50 is the least time-intensive and has the best accuracy (99%) in comparison to other classification performances. Interestingly, with a notable accuracy of 97.5%, MobileNetV2 also seems to be adequate in scenarios with less computational power than ResNet50. Finally, to interpret our black box model, we have used LIME as an explainable AI technique (XAI) to identify how the model is classifying the disease. The results emphasize the utility of transfer learning for optimizing diagnostic accuracy in skin disease classification, blending performance and resource efficiency as desired. The findings from this study may contribute to the development of automated tools for dermatological diagnosis and enable clinicians to reduce skin conditions in a timely manner.