AI-Powered Skin Lesion Diagnosis using Whale Optimization Algorithm Enhanced ResNet 50 for Cancer Prediction.

objectiveThe primary objective of this study is to enhance the accuracy and efficiency of binary skin lesion classification by optimizing the ResNet-50 convolutional neural network using the Whale Optimization Algorithm (WOA). This involves fine-tuning key hyperparameters such as learning rate, weights, and biases to improve predictive performance.

methodsThis study compares five CNN architectures: AlexNet, GoogleNet, VGG16, Resnet 50, and WOA-optimized Resnet 50. The dataset comprises 3,600 balanced images (224×244 resolution) of skin moles, evenly divided into 1,800 benign and 1,800 malignant cases.  The models were trained on an open-access dermoscopic dataset to categorize skin lesions.  WOA was applied to optimize Resnet 50's hyperparameters weight and bias learning rate. Model performance was analysed using accuracy, preci-sion, recall, F1 score, specificity, Matthews Correlation Coefficient (MCC), log loss, AUC-ROC, and infer-ence time. The confusion matrix was analyzed to assess misclassification rates.  Result: The WOA-optimized Resnet 50 outperformed all other models, achieving 98.29% accuracy, higher than standard Resnet 50 (90.13%), GoogleNet (87.1%), AlexNet (86.53%), and VGG16 (81.18%). It also demonstrated superior recall (99.31%), specificity (97.07%), and an AUC-ROC of 99.84%, indicating excellent classification capability. The MCC score (0.9657) confirmed strong predictive reliability. Addi-tionally, the optimized model achieved the lowest log loss (0.0512), ensuring high confidence in predictions. With an inference time of 0.1488 seconds, it was significantly faster than standard Resnet 50 (1.029 seconds), making it computationally efficient. The confusion matrix confirmed its reliability, showing min-imal false positives (7) and false negatives (2).

conclusionWOA-optimized Resnet 50 significantly improves accuracy, recall, specificity, and computational efficiency for binary skin lesion classification. Compared to traditional deep learning models, it offers superior predictive performance while maintaining fast inference time. These findings suggest that WOA-enhanced deep learning can enhance dermatological diagnostics, aiding early detection and clinical decision-making. Future research may explore its application for multi-class skin lesion classification and real-time medical imaging systems.