Optimized Convolutional Neural Network Models for Skin Lesion Classification
Juan Pablo Villa-Pulgarin1, Anderson Alberto Ruales-Torres1,2, Daniel Arias-Garzón1, Mario Alejandro Bravo-Ortiz1, Harold Brayan Arteaga-Arteaga1, Alejandro Mora-Rubio1, Jesus Alejandro Alzate-Grisales1, Esteban Mercado-Ruiz1, M. Hassaballah3, Simon Orozco-Arias4,5, Oscar Cardona-Morales1, Reinel Tabares-Soto1,*
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2131-2148, 2022, DOI:10.32604/cmc.2022.019529
- 27 September 2021
(This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models More >