Using a Convolutional Neural Network and Explainable AI to Diagnose Dementia Based on MRI Scans

TL;DR

This paper presents a CNN-based system with explainable AI features that accurately classifies dementia from MRI scans, enhancing interpretability and trust in medical diagnoses.

Contribution

The study introduces a CNN model trained on a large MRI dataset and an explainable AI method that visualizes influential image regions, improving diagnosis transparency.

Findings

Achieved 98% validation accuracy in classifying dementia types.

Developed an explainable AI algorithm that highlights key scan regions.

Demonstrated the model's ability to generalize to new MRI data.

Abstract

As the number of dementia patients rises, the need for accurate diagnostic procedures rises as well. Current methods, like using an MRI scan, rely on human input, which can be inaccurate. However, the decision logic behind machine learning algorithms and their outputs cannot be explained, as most operate in black-box models. Therefore, to increase the accuracy of diagnosing dementia through MRIs, a convolution neural network has been developed and trained using an open-source database of 6400 MRI scans divided into 4 dementia classes. The model, which attained a 98 percent validation accuracy, was shown to be well fit and able to generalize to new data. Furthermore, to aid in the visualization of the model output, an explainable AI algorithm was developed by visualizing the outputs of individual filters in each convolution layer, which highlighted regions of interest in the scan. These outputs do a great job of identifying the image features that contribute most to the model classification, thus allowing users to visualize and understand the results. Altogether, this combination of the convolution neural network and explainable AI algorithm creates a system that can be used in the medical field to not only aid in the proper classification of dementia but also allow everyone involved to visualize and understand the results.