SkinGuard 1: A Robust Multi-Class Deep Learning-Based Skin Cancer Detection Using a ResNet50-Based Model
Keywords:
Cancer, Cancer diagnosis, CNN, ResNet-50, Skin, SkinGuide 1Abstract
Skin cancer is an increasingly prevalent health concern, and early detection is essential for effective treatment and improved patient outcomes. Traditional diagnosis largely depends on dermatologists’ visual interpretation of skin lesions, which can be subjective and may result in variations in diagnosis or delays in treatment. This study presents an automated skin cancer detection system based on ResNet-50, a deep convolutional neural network architecture designed for image classification. The model was trained and evaluated using a dataset obtained from the International Skin Imaging Collaboration (ISIC). To improve accessibility and practical usability, the trained model was integrated into a user-friendly web application called SkinGuard, which enables users to upload skin lesion images and receive real-time predictions. Experimental results demonstrate a multi-class accuracy of 88%, precision of 91%, recall of 87%, and F1-score of 89%. These findings indicate that the proposed system has potential to support dermatologists in early skin cancer detection.
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