TY  - EJOU
AU  - Villa-Pulgarin, Juan Pablo 
AU  - Ruales-Torres, Anderson Alberto 
AU  - Arias-Garzón, Daniel 
AU  - Bravo-Ortiz, Mario Alejandro 
AU  - Arteaga-Arteaga, Harold Brayan 
AU  - Mora-Rubio, Alejandro 
AU  - Alzate-Grisales, Jesus Alejandro 
AU  - Mercado-Ruiz, Esteban 
AU  - Hassaballah, M. 
AU  - Orozco-Arias, Simon 
AU  - Cardona-Morales, Oscar 
AU  - Tabares-Soto, Reinel 

TI  - Optimized Convolutional Neural Network Models for Skin Lesion Classification
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 70
IS  - 2
SN  - 1546-2226

AB  - 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 based on DenseNet-201, Inception-ResNet-V2, and Inception-V3 are proposed and compared using the HAM10000 dataset. The results obtained by the three models demonstrate accuracies of 98%, 97%, and 96%, respectively. Finally, the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%. The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.
KW  - Deep learning; skin lesion; convolutional neural network; data augmentation; transfer learning

DO  - 10.32604/cmc.2022.019529