  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.
