| Title |
A novel channel-spatial deep residual attention network for the classification of neurodegenerative diseases from MRI scans |
| Authors |
Ibrar, Wardah ; Khan, Muhammad Attique ; Naqvi, Syeda Aimal ; Hussain, Zain ; Almuqren, Latifah ; Hussain, Amina ; Alhefdi, Mohammad ; Nam, Yunyoung |
| DOI |
10.1007/s12559-026-10652-0 |
| Full Text |
|
| Is Part of |
Cognitive computation.. New York : Springer. 2026, vol. 18, iss. 1, art. no. 106, p. 1-19.. ISSN 1866-9956. eISSN 1866-9964 |
| Keywords [eng] |
Alzheimer disease ; Attention Module ; Brain Tumor ; Classification ; Generalizability ; RAN Model |
| Abstract [eng] |
Personalized and precise classification of neurodegenerative diseases (Alzheimer’s and brain tumors) is crucial for early diagnosis and patient care. Brain tumors and Alzheimer’s disease are challenging to classify due to their varied shapes and features. In this work, we propose a novel four-block residual attention model for the classification of brain lesions and Alzheimer’s disease in magnetic resonance imaging (MRI). The 4-block attention model integrates a channel attention module (CAMB) and a spatial attention module (SAMB) into each of its four blocks. We also implemented the Residual Attention Network (RAN) to improve overall disease information further and achieve better classification and generalization. An ablation study was conducted to determine the optimal number of blocks, testing RAN models with 2, 3, 4, and 5 blocks for training and validation. The proposed 4-block RAN model achieved an accuracy of 96.8% on the Alzheimer dataset and 98.5% on the Figshare Brain dataset. With only 115 layers and 6.3 million parameters, the proposed model is significantly more compact than state-of-the-art architectures while maintaining improved classification performance for brain lesions and Alzheimer’s disease from MRI Scans. |
| Published |
New York : Springer |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
