A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Biswas, Koushik Pal, Ridal Patel, Shaswat Jha, Debesh Karri, Meghana Reza, Amit Durak, Gorkem Medetalibeyoglu, Alpay Antalek, Matthew Velichko, Yury Ladner, Daniela Borhani, Amir Bagci, Ulas |
| author_facet | Biswas, Koushik Pal, Ridal Patel, Shaswat Jha, Debesh Karri, Meghana Reza, Amit Durak, Gorkem Medetalibeyoglu, Alpay Antalek, Matthew Velichko, Yury Ladner, Daniela Borhani, Amir Bagci, Ulas |
| contents | Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases. Our study introduces a novel technique integrating momentum within residual blocks for enhanced training dynamics in medical image analysis. We applied our method in two distinct tasks: segmenting liver, lung, & colon data and classifying abdominal pelvic CT and MRI scans. The proposed approach has shown promising results, outperforming state-of-the-art methods on publicly available benchmarking datasets. For instance, in the lung segmentation dataset, our approach yielded significant enhancements over the TransNetR model, including a 5.72% increase in dice score, a 5.04% improvement in mean Intersection over Union (mIoU), an 8.02% improvement in recall, and a 4.42% improvement in precision. Hence, incorporating momentum led to state-of-the-art performance in both segmentation and classification tasks, representing a significant advancement in the field of medical imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation Biswas, Koushik Pal, Ridal Patel, Shaswat Jha, Debesh Karri, Meghana Reza, Amit Durak, Gorkem Medetalibeyoglu, Alpay Antalek, Matthew Velichko, Yury Ladner, Daniela Borhani, Amir Bagci, Ulas Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases. Our study introduces a novel technique integrating momentum within residual blocks for enhanced training dynamics in medical image analysis. We applied our method in two distinct tasks: segmenting liver, lung, & colon data and classifying abdominal pelvic CT and MRI scans. The proposed approach has shown promising results, outperforming state-of-the-art methods on publicly available benchmarking datasets. For instance, in the lung segmentation dataset, our approach yielded significant enhancements over the TransNetR model, including a 5.72% increase in dice score, a 5.04% improvement in mean Intersection over Union (mIoU), an 8.02% improvement in recall, and a 4.42% improvement in precision. Hence, incorporating momentum led to state-of-the-art performance in both segmentation and classification tasks, representing a significant advancement in the field of medical imaging. |
| title | A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2408.05692 |