Ensemble-Based Automated Skin Disease Classification Using Deep Convolutional Neural Networks

Skin disorders are common across all age groups and can lead to serious complications if not identified early. Conventional diagnosis methods rely on dermatologists' visual examination, which can be slow, subjective, and prone to error, particularly due to the visual similarity between different lesion types. These challenges are further amplified in areas with limited access to specialized healthcare. To address this issue, we propose a deep learning-based ensemble model for automatic skin disease classification. The Employs an ensemble of advanced deep convolutional models, namely Xception, InceptionV3, and ResNet50 each fine-tuned on the HAM10000 dermoscopic image dataset, which includes seven distinct skin lesion categories. To enhance the model’s performance and handle class imbalance, we apply techniques such as image augmentation, class weighting, random oversampling, and test-time augmentation (TTA). Final predictions are obtained through weighted soft voting across the ensemble. Additionally, Grad-CAM is employed to generate visual explanations of the model’s predictions, promoting transparency and aiding clinical interpretability. The proposed method achieves an accuracy of 97.22%, offering a robust and scalable solution that could support early diagnosis and improve dermatological services, especially in regions with limited healthcare infrastructure