Fractional White Smell Agent Optimization for CNN-Based Transfer Learning in Melanoma Classification
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Melanoma is the deadliest form of skin cancer, and early diagnosis and treatment can significantly reduce mortality rates. However, existing strategies for classifying melanoma from dermoscopic skin images still face significant challenges. Therefore, this study aims to develop an accurate method for melanoma classification using dermoscopic skin images. A novel melanoma classification framework, termed Fractional White Smell Agent Optimization-enabled Convolutional Neural Network-based Transfer Learning (FWSAO_CNN-based TL), is proposed. First, the input skin image is preprocessed using an Adaptive Kalman Filter. Subsequently, skin lesion segmentation is performed using LinkNet, where the network is trained using White Smell Agent Optimization (WSAO). Following segmentation, image augmentation is applied, and feature extraction is conducted. Finally, melanoma classification is performed using a CNN-based transfer learning model trained with the proposed Fractional White Smell Agent Optimization (FWSAO), which integrates the Fractional concept, Smell Agent Optimization (SAO), and White Shark Optimizer (WSO). The CNN utilizes hyperparameters derived from a pretrained GoogLeNet model. The performance of the proposed FWSAO_CNN-based TL framework was evaluated using accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). The proposed method achieved values of 91.565%, 90.090%, and 91.269%, respectively. Furthermore, the proposed model demonstrated performance improvements of 18.4%, 8.1%, 17.5%, 12.55%, 8.2%, and 6.23% compared with conventional approaches.
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