Differential Diagnosis of Cognitive Disorders using Deep Learning Techniques based on Neuroimaging Data

Background The worldwide increase in cognitive diseases has generated a greater demand for precise, non-invasive diagnostic tools with the ability of differentiating neurodegenerative diseases with similar clinical symptoms and subtle differences in brain anatomy. Alzheimer’s disease (AD), frontotemporal dementia (FTD), and Parkinson’s disease (PD) are typified by cognitive impairment, hence differential diagnosis becomes crucial. Methods This work utilizes three-dimensional convolutional neural networks (3D-CNNs) on 1,629 T1-weighted structural MRI images gathered from well-established datasets (ADNI, NIFD, and PPMI). Preprocessing involved format conversion, image registration, brain extraction, image cropping, volumetric downsampling, and intensity normalization. We built binary classification models for each disorder against healthy controls, and then a combined multiclass model for simultaneous discrimination between AD, FTD, PD, and controls. In order to prevent data leakage, subject-level data partitioning was performed instead of image-based splitting. Training was done without synthetic augmentation. With the application of Focal Loss and Maxout layers—innovations not utilized in previous research covered here—our strict preprocessing and architecture outperform many of the existing solutions in sensitivity and diagnostic accuracy. Results The binary models performed with very good accuracies: 98% (AD), 98% (FTD), and 93% (PD). The merged multiclass model achieved an overall accuracy of 95%, class-specific sensitivities of 100% (AD), 95% (FTD), and 95% (PD), and an AUC close to 1.00. Conclusions Our model’s strict preprocessing methods and novel architecture surpass numerous current methods, demonstrating the promise of 3D-CNN architectures as powerful clinical decision support systems for differential diagnosis of cognitive disorders.