Enhancing Automated and Early Detection of Alzheimer's Disease Using Out-Of-Distribution Detection

TL;DR

This paper improves early Alzheimer's detection by integrating out-of-distribution detection with CNN models, significantly reducing false diagnoses and enhancing reliability in MRI-based classification.

Contribution

It introduces a novel approach combining CNNs with OOD detection to improve accuracy and reduce false positives in Alzheimer's MRI diagnosis.

Findings

CNN with OOD detection achieved 98% detection accuracy

Reduced false positives in MRI classification

Flagged brain tumor images as OOD with 96% accuracy

Abstract

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.