Deep Ensemble Learning for Multiclass Skin Lesion Classification

Abstract

The skin, the largest organ of the body, acts as a protective shield against external stimuli. Skin lesions, which can be the result of inflammation, infection, tumors, or autoimmune conditions, can appear as rashes, spots, lumps, or scales, or remain asymptomatic until they become severe. Conventional diagnostic approaches such as visual inspection and palpation often lack accuracy. Artificial intelligence (AI) improves diagnostic precision by analyzing large volumes of skin images to detect subtle patterns that clinicians may not recognize. This study presents a multiclass skin lesion diagnostic model developed using the CSMUH dataset, which focuses on the Eastern population. The dataset was categorized into seven disease classes for model training. A total of 25 pre-trained models, including convolutional neural networks (CNNs) and vision transformers (ViTs), were fine-tuned. The top three models were combined into an ensemble using the hard and soft voting methods. To ensure reliability, the model was tested through five randomized experiments and validated using the holdout technique. The proposed ensemble model, Swin-ViT-EfficientNetB4, achieved the highest test accuracy of 98.5%, demonstrating strong potential for accurate and early skin lesion diagnosis.