Otimização em nível de bloco da ResNet com algoritmo genético para classificação de imagens endoscópicas

Abstract

Convolutional Neural Networks have demonstrated capability in identifying patterns in medical images, however, the need for explainable models remains critical in high-risk medical applications. This study presents an optimization framework based on Neural Ar- chitecture Search using a Genetic Algorithm to improve the performance of ResNet-152 and ResNet-50 on the Kvasir v2 dataset. The proposed approach incorporates image preproces- sing techniques and selective block optimization, achieving an accuracy of 97.75% on the validation set and 97.12% on the test set. In addition, the analysis of the optimization process revealed evidence of the structural relevance of specific bottleneck blocks, identified as being sensitive to dataset characteristics. The results of this study contribute to advan- cing global explainability in ResNet-based solutions by providing a deeper understanding of model behavior when applied to endoscopic images.