@Article{cmes.2025.069697,
AUTHOR = {Adeel Akram, Tallha Akram, Ghada Atteia, Ayman Qahmash, Sultan Alanazi, Faisal Mohammad Alotaibi},
TITLE = {Advancing Radiological Dermatology with an Optimized Ensemble Deep Learning Model for Skin Lesion Classification},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {2},
PAGES = {2311--2337},
URL = {http://www.techscience.com/CMES/v145n2/64552},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2025.069697}
}