Explainable Ensemble Deep Learning Framework for Melanoma Detection Using Thermoscopic Images

Abstract

Melanoma skin cancer is a significant public health concern due to its hazardous nature and the growing frequency of this cancer all over the world. For successful treatment early identification and precise medical diagnosis are still crucial. Dermatological field is now based on the technology of dermoscopic imaging, since this method gives a detailed visualization of skin lesions. Expert interpretation of these images requires manual analysis which is still subjective as it depends to a large extent on the doctor's experience. Machine learning and deep learning arms of Artificial Intelligence show encouraging results in the automation and enhanced diagnostic assessment. The main impediment to clinical rollout of these models continues to be: Such models contain decision processes that are still often hard to interpret. The research develops a dermatological skin cancer classification framework based on explanatory AI which incorporates ensemble learning to work with dermoscopic images. Te proposed system makes use of various deep learning system structures for accurate and resilient analysis and aids the decision-making clarity using both SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) explanation mechanisms. The researchers have set up a dual goal of providing both highly accurate diagnostic outputs, as well as easy-to-use systems and medical professional trust that stems from clear interpretive results.