Classification of Grade II and III Astrocytomas for Multi-modal MRI using Deep Volumetric Attention Networks.

Motivation: Diagnosis and grading of astrocytomas tumour present considerable challenges. Manual grading is time-consuming and error prone. Preoperative MRIs are a useful, yet deep learning presents challenges due to computing limitations and complex architecture. Goal(s): Study introduces novel multimodal MRI classification for grade II and III astrocytomas, aiming to improve accuracy, reduce complexity, and address interclass homogeneity via attention mechanism. Approach: Single slice from eight MRI modalities forms a three-dimensional cube. Normalized, iPCA processed, and passed to deep model with volumetric attention network. Results: The DVA using advanced and traditional MRI information outperforms existing models achieving an overall accuracy of 77% using five-fold cross-validation. Impact: The proposed multimodal MRI classification approach enhances astrocytoma diagnosis and grading. The deep volumetric attention model improves accuracy, reduces model complexity, and holds potential for trustworthiness impacts in clinical practice.