Improving Skin Lesion Diagnosis with Grasshopper-Optimized ResNet-50 Architecture

Skin lesion diagnosis helps doctors find and treat melanoma and skin cancer early. Computer systems that use CNNs and deep learning methods recently proved highly capable at identifying different types of skin lesions. This study develops an enhanced approach by combining the ResNet-50 architecture with the GOA algorithm to improve diagnostic outcomes and broaden the ability to detect various conditions. ResNet-50, a popular CNN architecture, drawing inspiration from the foraging behaviour of grasshoppers, which effectively traverse search areas to discover optimum solutions. With the goal of improving feature extraction and classification performance, the Grasshopper-Optimized ResNet-50 model attempts to address typical deep learning difficulties including overfitting and convergence stagnation. The GOA improves hyperparameter tuning for ResNet-50 by using grasshoppers' natural searching method in optimization. This system optimizes feature acquisitions while preventing model fitting errors and learning rate slowdowns. The framework demonstrates excellent results in data testing by reaching 97% accuracy and 93% F1 while showing strong improvements in performance measurement. Research shows how optimization algorithms based on natural phenomena boost the effectiveness of deep learning methods in medical image analysis. The Grasshopper-Optimized ResNet-50 model demonstrates strong capabilities in skin lesion identification and brings valuable solutions to medical systems and patient care.