Godwin Ekuma

Neuroimaging information plays a crucial role in the diagnosis and prognosis of Alzheimer's disease (AD). Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging technique that uses radio waves to reveal fine details of brain anatomy and pathology. Radiologists can use information in MRI along with other clinical data to determine if a patient has this disease or not. However, efforts are being made by researchers to deploy computer-aided diagnostic tools to aid radiologists in MRI interpretation and reduce human errors. Deep CNNs have become the state-of-the-art technique for medical imaging classification on different imaging modalities for both binary and multiclass problems. Deep CNNs can extract spatial features from image data in a hierarchical manner, with deeper layers learning more features that are potentially more relevant to the classification application. This study evaluates an explainable deep CNN-based learning model for the classification of AD severity using MRI. The deep learning models are based on three pre-trained neural network architectures: DenseNet121, DenseNet169, and Inception-ResNet-v2. The framework achieved high sensitivity and specificity on the test sample of subjects with varying levels of AD severity. The deep learning framework shows promise in the classification of MR images from subjects with AD.