Classification of Melanoma Cancer Using Deep Convolutional Neural Networks

Abstract

Accurate detection of skin diseases is crucial in healthcare, with early diagnosis being particularly vital for effective treatment. Melanoma, a form of skin cancer with a high potential for metastasis, requires early detection to significantly improve treatment success and prevent further spread across the skin. This study investigates the application of machine learning techniques to diagnose skin lesions, focusing on differentiating between benign moles and malignant melanoma. A Convolutional Neural Network (CNN) model was developed to explore machine learning's efficacy in this context. The initial model featured a primary architecture, progressively refined by adding additional layers and filters to increase its complexity. This iterative enhancement aimed to improve the model’s capability to extract and analyze features from skin images. Each model configuration was meticulously evaluated through a series of experiments to determine its diagnostic performance. The results revealed that the proposed CNN model achieved a high accuracy rate of 91\%. This significant finding demonstrates the effectiveness of machine learning approaches in the early diagnosis and management of melanoma. The study confirms that advanced CNN architectures can enhance diagnostic precision, thereby contributing to improved patient outcomes in detecting and treating skin diseases.

Keywords

• CNN
• artificial learning
• melanoma
• mole (nevus)
• skin cancer

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