Enhanced medical image diagnosis using a deep learning approach based on autoencoders

Abstract

Medical image analysis is essential for disease diagnosis, but its interpretation remains challenging. This thesis investigates the use of deep learning, particularly Convolutional Neural Networks (CNNs) and autoencoders, to automate medical image classification. Two case studies are presented: brain tumor classification from MRI images and pneumonia detection from chest X-ray images. The obtained results demonstrate high classification accuracy and confirm the potential of these approaches for developing computer-aided diagnosis systems.