Informatics Publishing Limited

M. Kumar ¹, Jayaraj Ramasamy ², M. Praveen Kumar ³, B.K. Harsha ⁴

Affiliations
1 Department of Electronics and Communication Engineering, Chettinad College of Engineering and Technology, IN
2 Faculty of Engineering and Technology, Botho University, BW
3 Department of Control and Automation, Vellore Institute of Technology, Vellore, IN
4 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN

Abstract

Brain cancer is a life-threatening disease that requires accurate and efficient segmentation methods for effective diagnosis and treatment planning. In this study, we propose an improved segmentation method for brain cancer using Capsule Neural Networks (CapsNets). CapsNets are a promising alternative to traditional convolutional neural networks (CNNs) as they capture spatial relationships between features more effectively. However, existing CapsNet-based segmentation methods suffer from limitations such as low segmentation accuracy and high computational complexity. To address these limitations, we introduce an improved CapsNet architecture that incorporates dynamic routing and attention mechanisms. The dynamic routing algorithm enhances the routing process between capsules, allowing for better feature representation and improved segmentation accuracy. Additionally, the attention mechanism focuses the network’s attention on important regions, reducing the computational complexity without sacrificing segmentation quality. We evaluate the proposed method on a publicly available brain cancer dataset and compare its performance against state-of-the-art segmentation approaches. The experimental results demonstrate that our method achieves superior segmentation accuracy and outperforms existing methods in terms of Dice coefficient and Hausdorff distance. Furthermore, our method demonstrates faster convergence and reduced computational complexity compared to previous CapsNet-based approaches. In conclusion, this study presents an improved segmentation method for brain cancer using Capsule Neural Networks. The proposed method addresses the limitations of existing CapsNet-based approaches by incorporating dynamic routing and attention mechanisms. The experimental results validate the effectiveness of our method, showcasing superior segmentation accuracy and reduced computational complexity. The improved segmentation method has the potential to enhance the diagnosis and treatment planning of brain cancer, ultimately contributing to improved patient outcomes.

Keywords

Brain, Segmentation, Capsule Network, Capsules.

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