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An evolutionary explainable deep learning approach for Alzheimer's MRI classification by Shakila Shojaei; Mohammad Saniee Abadeh; Zahra Momeni is a Computer Science article available to read on EtoBox.

What is An evolutionary explainable deep learning approach for Alzheimer's MRI classification about?

Deep Neural Networks (DNN) are prominent Machine Learning (ML) algorithms widely used, especially in medical tasks. Among them, Convolutional Neural Networks (CNN) are well-known for image-based tasks and have shown excellent performance. In contrast to this remarkable performance, one of their most fundamental drawbacks is their inability to clarify the cause of their outputs. Moreover, each ML algorithm needs to present an explanation of its output to the users to increase its reliability. Occlusion Map is a method used for this purpose and aims to find regions of an image that have a significant impact on determining the network's output, which does this through an iterative process of occluding different regions of images. In this study, we used Magnetic Resonance Imaging (MRI) scans from Alzheimer's Disease Neuroimaging Initiative (ADNI) and trained a 3D-CNN model to diagnose Alzheimer's Disease (AD) patients from cognitively normal (CN) subjects. We tried to combine a genetic algorithm-based Occlusion Map method with a set of Backpropagation-based explainability methods, and ultimately, we found a brain mask for AD patients. Also, by comparing the extracted brain regions with

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Author

Shakila Shojaei; Mohammad Saniee Abadeh; Zahra Momeni

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)