Convolutional Neural Network Model for Skin Cancer Diagnosis in a Dermatological Center

Cabrejos-Yalán V.M.; Rodriguez C. · Mathematical Modelling of Engineering Problems (2024) · DOI: 10.18280/mmep.111112

Resumen

In confronting the global health challenge posed by skin cancer, early and accurate diagnosis is paramount. This research introduces an advanced Convolutional Neural Network (CNN) model optimized for skin cancer diagnosis using dermatological images. The innovation lies in applying state-of-the-art pruning techniques, specifically magnitude-based weight pruning and quantization, to refine the model's efficacy and computational efficiency. The model exhibited exceptional performance on a rigorous dataset, achieving an AUC (Area Under the Curve) value of 0.99. The acquired score indicates an exceptionally high degree of competence in distinguishing benign skin conditions from malignant ones. Critical performance indicators—with values of 0.9820 for precision, 0.9815 for recall, and 0.9812 for F1-score—offer supplementary substantiation concerning the dependability and accuracy of the model. Notably, the refined model maintained an impressive accuracy rate of 0.9815 post-pruning, validating the effectiveness of the pruning process. The employment of these pruning methods has substantially streamlined the model without compromising diagnostic accuracy, demonstrating the integration of machine learning can significantly enhance medical imaging. The findings of this study not only mark a leap forward in skin cancer diagnostics but also enrich the discourse on intelligent systems in healthcare, advocating for broader adoption and continued development.

Instituciones: Universidad Nacional Federico Villarreal