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Robust cascade bidirectional triple capsule network with OOA for deep neural network-based improved brain tumor
Kavitha Thangavel1, Mathivanan Murugavelu2, Selvin Christalin Nelson3
1Department of Computer Applications, Kongu Engineering College, Perundurai, Tamil Nadu, India.
Background:
Abnormal growth of brain cells may produce serious neurological symptoms (migraines, seizures, and cognitive impairments), which are called brain tumors. Early and precise diagnosis is of utmost importance in enhancing prognosis and treatment options such as surgery, radiation, or chemotherapy. Although the current systems have improved in Deep Learning (DL), they continue to record poor accuracy and high false positive rates, which warrant more trustworthy solutions.
Purpose:
In this paper, the authors propose a Robust Triple Extraction with Cascade Bidirectional Capsule Network and Osprey Optimization Algorithm (RT-CBCN-OOA) to achieve a higher quality, accuracy, and dependability of brain tumor detection and classification.
Methods:
The pre-processing of the BraTS and Figshare MRI images is performed with the help of the Modified Square Root Sage-Husa Adaptive Kalman Filter (MSRS-HAKF) in order to eliminate noise and enhance image clarity. Dual-Domain Attention CNN based on EfficientNet-B3 CNN (EN-B3 CNN-2DA) is used to extract features and segment and classify these features using the Geometric Algebra Transformer-based Robust Cascade Bidirectional Triple Capsule Network with Triple Attention (GAT-RCBTCN-TA). The Osprey Optimization Algorithm (OOA) optimizes the performance of model weights.
Results And Conclusion:
The proposed RT-CBCN-OOA has a recall and accuracy of 99.9 and 99.8, respectively, which is better than the current models. It provides a powerful, precise, and efficient brain tumor detection, which proves to have a great future in clinical use in medical imaging and diagnosis.
