Informatics Publishing Limited

B.K. Harsha ¹, B. Srinivasa Rao ², S. Tamijeselvan ³, M. Ganesha ⁴, Nihar Ranjan Behera ⁵

Affiliations
1 School of Computing and Information Technology, Reva University, IN
2 Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, IN
3 Department of Radiography, Mother Theresa PG and Research Institute of Health Sciences, IN
4 Department of Computer Science and Engineering, A J Institute of Engineering and Technology, IN
5 Department of Business and Management, Swiss School of Business and Management Geneva, CH

Abstract

Image representation and rendering have become critical in numerous applications such as virtual reality, medical imaging, and computer graphics. Traditional rendering techniques often face challenges in efficiently handling complex scenes and achieving photorealistic results while maintaining low computational costs. The problem lies in the high-dimensional nature of image data, leading to slow processing times and reduced scalability. This research presents an AI-enhanced technique called Linear RepRender, which leverages deep learning to transform high-dimensional image representations into simplified linear forms for faster rendering. The proposed method employs a combination of convolutional neural networks (CNNs) and linear regression models to reduce image complexity. Specifically, the CNN extracts low-level and high-level features from the image, while the linear regression step approximates the scene’s core visual elements. This hybrid approach significantly improves rendering speed without sacrificing image quality. Furthermore, the method incorporates a loss function optimized for minimizing discrepancies between the rendered and ground truth images. Experimental results demonstrate that Linear RepRender outperforms traditional rendering algorithms, such as ray tracing and rasterization, in terms of computational efficiency and visual accuracy. On a dataset of complex 3D scenes, the proposed method achieved a 35% reduction in rendering time and a 22% improvement in peak signal-to-noise ratio (PSNR) compared to stateof-the-art methods. Additionally, Linear RepRender was able to handle up to 1.5 million polygons per scene with minimal visual artifacts, making it suitable for real-time applications.

Keywords

AI-Enhanced Rendering, Image Representation, Linear Regression, Convolutional Neural Networks, Real-Time Rendering

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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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