Explainable Deep Learning for Dermatology: Psoriasis vs. Eczema with Grad-CAM

Eczema and Psoriasis are two of the most prevalent inflammatory dermatological conditions with distinctive clinical features. However, their features may overlay with each other's symptoms often leading to erroneous assessment. Therefore, precise identification between the two classes is quite challenging and paramount. In recent years, there has been tremendous success in implementing deep learning tools in dermatology for evaluating skin disease variants, however the reliability of AI tools still remains questionable. In this research, we explore and compare 5 pre-trained DL models: ResNeXt-50 32x4d, Shufflenetv2-x1.0, EfficientNet-B2, MobileNetV2, EfficientNet-B4 using transfer learning to precisely categorize the two dermatological conditions i.e. Eczema and Psoriasis. Out of all, ResNeXt-50 32x4d model attained superior classification performance with 98.39% train accuracy rate and 88.41% test accuracy rate. For better comprehension and interpretability of the model's acquired layer-wise features and the yielded outcome, Explainable AI i.e. GradCAM is opted here. The outcomes of this study are highly significant towards enhanced skin prognosis and showcases that incorporating Grad-CAM may aid in accurately addressing these skin conditions severity at early stages for improved quality of life for afflicted individuals.