Advances in Diagnostic Imaging: Integrating Explainable AI to Optimize Convolutional Networks

Abstract

Convolutional neural networks (CNNs) are fundamental in deep learning, especially in computer vision tasks.They stand out for their ability to extract spatial features from data. However,their complexity has generated the need for explainability in artificial intelligence (XAI), which seeks to interpret and understand their predictions.This work is carried out with the purpose of knowing the applicability of convolutional networks in the classification of Medical images,specifically, endoscopi images already previously collected, and through fine-tunning we will explore architectures that present better performance. Afterwards, we implement the AI explainability techniques,together with the Language model we will assess the process of automating the creation of the medical report through the graphic representations created.