A Robust Explainable Deep Learning Ensemble for Early Skin Cancer Diagnosis

Abstract

Skin cancer is one of the most common types of malignancies around the world, and the ability to detect skin cancers in an early stage is crucial for improving overall patient outcomes. This study introduces a hybrid deep learning framework that utilizes self-supervised pretraining, multi-architecture ensemble learning, and explainable AI approaches to enable accurate and interpretable skin cancer diagnosis. This framework uses SimCLR-based contrastive learning techniques to generate powerful feature representations from large data sets of unlabeled images of dermatoscopic images before implementing either supervised fine-tuning processes or feature-level fusion processes on three different types of architectures (EfficientNetV2-L, Swin Transformer, and ConvNeXt). In order to classify patients using the features derived from the different architectures, a meta-learning classifying component based on LightGBM is built into the model and provides explainability through the Grad-CAM and SHAP explainable AI methods. The results of the experiments performed with benchmark datasets (ISIC, and HAM10000) demonstrate the proposed method outperformed previously established baseline models by a wide margin, achieving 94.5% accuracy, 92.55% precision, and 93.26% recall, providing evidence of the robustness, high sensitivity, and reliability of the proposed method in the early detection of skin cancer.