  More than 10.7% of people aged 65 and older are affected by Alzheimer's
disease. Early diagnosis and treatment are crucial as most Alzheimer's patients
are unaware of having it until the effects become detrimental. AI has been
known to use magnetic resonance imaging (MRI) to diagnose Alzheimer's. However,
models which produce low rates of false diagnoses are critical to prevent
unnecessary treatments. Thus, we trained supervised Random Forest models with
segmented brain volumes and Convolutional Neural Network (CNN) outputs to
classify different Alzheimer's stages. We then applied out-of-distribution
(OOD) detection to the CNN model, enabling it to report OOD if
misclassification is likely, thereby reducing false diagnoses. With an accuracy
of 98% for detection and 95% for classification, our model based on CNN results
outperformed our segmented volume model, which had detection and classification
accuracies of 93% and 87%, respectively. Applying OOD detection to the CNN
model enabled it to flag brain tumor images as OOD with 96% accuracy and
minimal overall accuracy reduction. By using OOD detection to enhance the
reliability of MRI classification using CNNs, we lowered the rate of false
positives and eliminated a significant disadvantage of using Machine Learning
models for healthcare tasks. Source code available upon request.
