CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy

• URL: http://arxiv.org/abs/2410.20231v3
• Date: Mon, 30 Dec 2024 12:48:44 GMT
• Title: CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy
• Authors: Ishita Harish, Saurav Mishra, Neha Bhadoria, Rithik Kumar, Madhav Arora, Syed Rameem Zahra, Ankur Gupta,
• Abstract summary: We propose an ensemble-based approach to improve diagnostic accuracy in analyzing complex image datasets.<n>We leverage the unique feature extraction capabilities of each model to enhance the overall accuracy.<n>By using these methods, the proposed framework, CAVE-Net, provides robust feature discrimination and improved classification results.
• Score: 0.1937002985471497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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