Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model

2025/06/01 by Javier Salazar Cavazos, Cavazos, Javier Salazar, Scott Peltier +1
Computer Science · Engineering · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Imaging and Analysis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.02060

openalex publication_date 2025/06/01 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

Abstract

Previous works in the literature apply 3D spatial-only models on 4D functional MRI data leading to possible sub-par feature extraction to be used for downstream tasks like classification. In this work, we aim to develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels that not only learn spatial information but in addition also capture temporal dynamics. Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D models. The 4D CNN model improves Alzheimers disease diagnosis for rs-fMRI data, enabling earlier detection and better interventions. Future research could explore task-based fMRI applications and regression tasks, enhancing understanding of cognitive performance and disease progression.

Citations

• A ConvNet for the 2020s
• A ConvNet for the 2020s
• 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum\n Disorder Classification
• 4D CNN for semantic segmentation of cardiac volumetric sequences
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

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