Advancing Radiological Dermatology with an Optimized Ensemble Deep Learning Model for Skin Lesion Classification

Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score in melanoma detection. The proposed method achieved a classification accuracy of 98.5%, evaluated using an LSVM classifier on the Argentina Skin Lesion dataset, underscoring the robustness of the proposed methodology. The proposed approach offers a scalable and computationally efficient solution for automated skin lesion classification, thereby contributing to improved clinical decision-making and enhanced patient outcomes. By aligning artificial intelligence with radiation-based medical imaging and bioinformatics, this research advances dermatological computer-aided diagnosis (CAD) systems, minimizing misclassification rates and supporting early skin cancer detection. The proposed approach provides a scalable and computationally efficient solution for automated skin lesion analysis, contributing to improved clinical decision-making and enhanced patient outcomes.