TY  - JOUR
AU  - Shabu, S.L. Jany
AU  - Lakshmanan, L.
AU  - Refonaa, J.
AU  - Dhamodaran, S.
AU  - Li, Aimin
AU  - Mallik, Saurav
PY  - 2026
DA  - 2026/08/28
TI  - Precision Brain Tumour Diagnosis with Convolutional Recurrent Attention Networks
JO  - OBM Neurobiology
SP  - 351
VL  - 10
IS  - 03
AB  - Histological heterogeneity and complex imaging patterns make brain tumour diagnosis difficult. In order to make timely clinical decisions and plan personalised treatment, MRI multi-class brain tumour classification must be accurate. Expert evaluation in conventional image interpretation increases diagnostic burden and inter-observer variability. While current AI-based tumour classification methods have improved, learning discriminative spatial-contextual representations and giving clinically relevant model interpretability remain challenges. This article offers Convolutional Recurrent Attention Network (CRANet) for multi-class brain tumour classification using T1-weighted MRI data to overcome these issues. Convolutional Neural Networks (CNNs) extract hierarchical spatial features, Recurrent Neural Networks (RNNs) model contextual dependencies among deep feature representations, and a Channel-Spatial Attention Module emphasises diagnostically informative features during classification in the proposed architecture. Cross-validation evaluates generalisation performance, whereas noise reduction, intensity normalisation, and data augmentation improve data quality and model resilience. Experiments show that CRANet outperforms state-of-the-art deep learning methods in conventional diagnostic measures. Attention activation maps improve model transparency and clinician interpretation by visualising picture areas contributing to classification decisions, without localising tumours.
SN  - 2573-4407
UR  - https://doi.org/10.21926/obm.neurobiol.2603351
DO  - 10.21926/obm.neurobiol.2603351
ID  - Shabu2026
ER  -