Artificial intelligence-assisted detection model for melanoma diagnosis using deep learning techniques

The progressive depletion of the ozone layer poses a significant threat to both human health and the environment. Prolonged exposure to ultraviolet radiation increases the risk of developing skin cancer, particularly melanoma. Early diagnosis and vigilant monitoring play a crucial role in the successful treatment of melanoma. Effective diagnostic strategies need to be implemented to curb the rising incidence of this disease worldwide. In this work, we propose an artificial intelligence-based detection model that employs deep learning techniques to accurately monitor nevi with characteristics that may indicate the presence of melanoma. A comprehensive dataset comprising 8598 images was utilized for the model development. The dataset underwent training, validation, and testing processes, employing the algorithms such as AlexNet, MobileNet, ResNet, VGG16, and VGG19, as documented in current literature. Among these algorithms, the MobileNet model demonstrated superior performance, achieving an accuracy of %84.94 after completing the training and testing phases. Future plans involve integrating this model with a desktop program compatible with various operating systems, thereby establishing a practical detection system. The proposed model has the potential to aid qualified healthcare professionals in the diagnosis of melanoma. Furthermore, we envision the development of a mobile application to facilitate melanoma detection in home environments, providing added convenience and accessibility.