Towards Accurate Detection and Classification of Skin Cancer Using AI-Powered Image Analysis
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Context: Skin cancer is one of the most common and lethal forms of cancer if not diagnosed early. Detection at an early stage can mean a world of difference in survival. Despite advances in technology, most areas continue to face a lack of available dermatology specialists, making timely diagnosis a challenge. This gap between the need for early detection and the limited availability of experts highlights the urgency for automated, reliable diagnostic tools. Objective: In response to this need, the purpose of this work is to create a fast and accurate system for skin cancer detection that distinguishes between malignant and benign lesions. Method: This study, based on a Convolutional Neural Network (CNN), addresses some challenges, including limited data, image quality issues, and classification. Results: A balanced training set was used to train the model, which achieved a test accuracy of 88.60% and a macro-averaged Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 0.95. Other significant metrics, such as precision, recall, and F1-score also validated the performance of the model. We developed a web-based interface to enable practical deployment and usability for real-world applications. Conclusions: The findings demonstrate the potential of deep learning techniques to enhance skin cancer detection and serve as a point of reference for future research on multi-class classification and real-time diagnostic platforms.
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