CAVE: Classifying Abnormalities in Video Capsule Endoscopy

• URL: http://arxiv.org/abs/2410.20231v1
• Date: Sat, 26 Oct 2024 17:25:08 GMT
• Title: CAVE: Classifying Abnormalities in Video Capsule Endoscopy
• Authors: Ishita Harish, Saurav Mishra, Neha Bhadoria, Rithik Kumar, Madhav Arora, Syed Rameem Zahra, Ankur Gupta,
• Abstract summary: In this study, we explore an ensemble-based approach to improve classification accuracy in complex image datasets. We leverage the unique feature-extraction capabilities of each model to enhance the overall accuracy. Experimental evaluations demonstrate that the ensemble achieves higher accuracy and robustness across challenging and imbalanced classes.
• Score: 0.1937002985471497
• License:
• Abstract: In this study, we explore an ensemble-based approach to improve classification accuracy in complex image datasets. Utilizing a Convolutional Block Attention Module (CBAM) alongside a Deep Neural Network (DNN) we leverage the unique feature-extraction capabilities of each model to enhance the overall accuracy. Additional models, such as Random Forest, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), are introduced to further diversify the predictive power of our ensemble. By leveraging these methods, the proposed approach provides robust feature discrimination and improved classification results. Experimental evaluations demonstrate that the ensemble achieves higher accuracy and robustness across challenging and imbalanced classes, showing significant promise for broader applications in computer vision tasks.

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