Keywords: Diagnosis/Prediction, Brain, Volumetric Attention Network, Deep Learning, Astrocytomas, Glioma, Classification
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.
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