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All Journal International Journal of Electrical and Computer Engineering
Pallavi, Gundala
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Computer Science & IT Electrical & Electronics Engineering

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A fusion of cross-shaped window attention block and enhanced 3D U-Net for brain tumor segmentation Polaki, Ramya; Rangarajan, Prasanna Kumar; Pallavi, Gundala; Rajasekhar, Elakkiya; Altalbe, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7103-7115

Abstract

Brain tumor diagnosis and treatment are primarily reliant on medical imaging, necessitating precise segmentation methodologies for practical clinical solutions. Tumor boundaries are difficult to consistently identify, even with breakthroughs in deep learning. To address this challenge, we propose a novel approach that combines an upgraded 3D U-Net architecture for brain tumor segmentation with cross-shaped window attention (CSWA-U-Net). Current segmentation techniques have limitations, particularly in capturing amorphous tumor shapes and fuzzy boundaries. Our strategy aims to overcome these constraints by combining the complementary capabilities of the expanded 3D U-Net, which is efficient at managing volumetric data and maintaining spatial features, with the cross-shaped window attention, which is well-known for capturing long-range relationships and contextual information. We evaluate our method's efficacy using a variety of performance measures, including specificity, sensitivity, and the Dice score. Our results demonstrate increased performance, with Dice scores of 94.7% for the whole tumor, 93.4% for the enhanced tumor region, and 90.5% for the tumor core. Furthermore, our technique has high sensitivity and specificity, highlighting its potential for improving medical imaging analysis.
The social media sentiment analysis framework: deep learning for sentiment analysis on social media Rangarjan, Prasanna Kumar; Gurusamy, Bharathi mohan; Muthurasu, Gayathri; Mohan, Rithani; Pallavi, Gundala; Vijayakumar, Sulochana; Altalbe, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3394-3405

Abstract

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.
Co-Authors Altalbe, Ali Gurusamy, Bharathi Mohan Mohan, Rithani Muthurasu, Gayathri Polaki, Ramya Rajasekhar, Elakkiya Rangarajan, Prasanna Kumar Rangarjan, Prasanna Kumar Vijayakumar, Sulochana