Cutaneous melanoma is a dermatological disease that affects a large portion of the world's population and is characterized by its high capacity for dissemination and aggressiveness, especially when not detected early. Given this need, the objective was to develop a mobile application based on convolutional neural networks for the initial assessment of this condition. Therefore, the percentage increase in sensitivity, specificity, and accuracy was evaluated. The research employed a quantitative approach and a pre-experimental design. The study variable was the initial assessment of cutaneous melanoma. The sample consisted of 120 images: 60 images from patients with melanoma-positive and 60 images from patients with melanoma-negative. The results of the implementation showed an increase in sensitivity of 0.729%, specificity of 3.626%, and accuracy of 2.631%. In conclusion, the adoption of the mobile application based on convolutional neural networks strengthens the initial assessment of cutaneous melanoma by optimizing these indicators.
Farro-Llanos et al. (Thu,) studied this question.