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

ABSTRACT:

Objective

The 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.

Methods

This 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 r

SUBMITTER: Urundai Meeran SB 

PROVIDER: S-EPMC12661256 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature