CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917880827740160 |
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| author | Harish, Ishita Mishra, Saurav Bhadoria, Neha Kumar, Rithik Arora, Madhav Zahra, Syed Rameem Gupta, Ankur |
| author_facet | Harish, Ishita Mishra, Saurav Bhadoria, Neha Kumar, Rithik Arora, Madhav Zahra, Syed Rameem Gupta, Ankur |
| contents | Accurate classification of medical images is critical for detecting abnormalities in the gastrointestinal tract, a domain where misclassification can significantly impact patient outcomes. We propose an ensemble-based approach to improve diagnostic accuracy in analyzing complex image datasets. Using a Convolutional Block Attention Module along with a Deep Neural Network, we leverage the unique feature extraction capabilities of each model to enhance the overall accuracy. The classification models, such as Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbors are introduced to further diversify the predictive power of proposed ensemble. By using these methods, the proposed framework, CAVE-Net, provides robust feature discrimination and improved classification results. Experimental evaluations demonstrate that the CAVE-Net achieves high accuracy and robustness across challenging and imbalanced classes, showing significant promise for broader applications in computer vision tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20231 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy Harish, Ishita Mishra, Saurav Bhadoria, Neha Kumar, Rithik Arora, Madhav Zahra, Syed Rameem Gupta, Ankur Computer Vision and Pattern Recognition Accurate classification of medical images is critical for detecting abnormalities in the gastrointestinal tract, a domain where misclassification can significantly impact patient outcomes. We propose an ensemble-based approach to improve diagnostic accuracy in analyzing complex image datasets. Using a Convolutional Block Attention Module along with a Deep Neural Network, we leverage the unique feature extraction capabilities of each model to enhance the overall accuracy. The classification models, such as Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbors are introduced to further diversify the predictive power of proposed ensemble. By using these methods, the proposed framework, CAVE-Net, provides robust feature discrimination and improved classification results. Experimental evaluations demonstrate that the CAVE-Net achieves high accuracy and robustness across challenging and imbalanced classes, showing significant promise for broader applications in computer vision tasks. |
| title | CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.20231 |