Melanoma Detection Through Convolutional Neural Network Models

Abstract

Melanoma, a severe form of skin cancer, is typically diagnosed by dermatologists through visual assessment. However, variations in expertise and limited access to quality healthcare services can result in inaccurate diagnoses. This study proposes a Convolutional Neural Network (CNN)-based approach for detecting melanoma using dermoscopic image datasets. The developed model demonstrates high performance in terms of accuracy, sensitivity, and specificity, showing clear improvement over conventional diagnostic techniques. Trained on a large and diverse dataset, the proposed CNN architecture is robust against variations in image quality and patient characteristics. The findings highlight the potential of deep learning models, particularly CNNs, in supporting clinical decision-making by enabling early and reliable detection of melanoma, thereby contributing to improved patient outcomes and survival rates.