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

# metadata.dc.description[].value
Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2023-2024, Tutor: Esteban Vegas Lozano

# metadata.dc.description.abstract[].value
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.

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Estadística UB-UPC

# metadata.dc.title[].value
Advances in Diagnostic Imaging: Integrating Explainable AI to Optimize Convolutional Networks