A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation
- Title
- A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation
- Creator
- Sille R.; Choudhury T.; Sharma A.; Chauhan P.; Tomar R.; Sharma D.
- Description
- Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio (PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The models accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created. 2023 by the authors.
- Source
- Medicina (Lithuania), Vol-59, No. 1
- Date
- 2023-01-01
- Publisher
- MDPI
- Subject
- autoencoder; brain MRI; deep learning; generative adversarial learning; tumour segmentation
- Coverage
- Sille R., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Choudhury T., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Sharma A., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Chauhan P., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Tomar R., Persistent Systems, India, 411057, India; Sharma D., School of Business and Management, CHRIST University, Bangalore, 560074, India
- Rights
- All Open Access; Gold Open Access; Green Open Access
- Relation
- ISSN: 1010660X; PubMed ID: 36676743
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Sille R.; Choudhury T.; Sharma A.; Chauhan P.; Tomar R.; Sharma D., “A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/14699.