XAI-SKIN: An Approaches for the Diagnosis and Classification of Skin Diseases based on LIME Method

Abstract

Nowadays, it is challenging to make an accurate diagnosis of skin conditions. Precision in diagnosis is essential for improved prognosis and illness control. Skin cancer is one of the most prevalent cancers worldwide, and its rising incidence rates are posing a significant challenge to healthcare systems. Precise diagnosis and early detection are essential for both patient outcomes and effective therapy. Individuals and the healthcare system are severely impacted by the misdiagnoses. However, because skin illnesses are complicated and include a wide variety of symptoms and subjective interpretation, dermatologists have more challenges in identifying them. In order to prevent unnecessary procedures, provide appropriate care, and save healthcare resources, it becomes more challenging to precisely define critical attributes.  Skin cancer may be successfully identified from a lesion picture using deep learning. its practical use is constrained by the lack of justification for its conclusions.  Explainable AI (XAI) approaches specifically designed for skin disease diagnosis are used to train the AI models on skin disease data sets. In order to help physicians comprehend AI-driven diagnoses and foster confidence and collaboration with AI diagnostic tools, we provide them transparency and interpretability. ResNet50V2, VGG16, InceptionV3, and InceptionResNetV2 are the four pre-trained models that have been used. Due to the uncertainty of these models, this work also attempts to use Explainable Artificial Intelligence, which is based on Local Interpretable Model-Agnostic Explanation (LIME)  , to explain the predictions of these models.