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 frame...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.展开更多
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP2/283/46funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R748),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘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.
