Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations

• URL: http://arxiv.org/abs/2410.19363v1
• Date: Fri, 25 Oct 2024 08:01:35 GMT
• Title: Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations
• Authors: Lakshmi Srinivas Panchananam, Praveen Kumar Chandaliya, Kishor Upla, Kiran Raja,
• Abstract summary: Abnormalities in the gastrointestinal tract significantly influence the patient's health and require a timely diagnosis. The work presents the process of developing and evaluating a novel model designed to classify gastrointestinal anomalies from a video frame. integration of Omni Dimensional Gated Attention (OGA) mechanism and Wavelet transformation techniques into the model's architecture allowed the model to focus on the most critical areas. The model's performance is benchmarked against two base models, VGG16 and ResNet50, demonstrating its enhanced ability to identify and classify a range of gastrointestinal abnormalities accurately.
• Score: 1.5146068448101746
• License:
• Abstract: Abnormalities in the gastrointestinal tract significantly influence the patient's health and require a timely diagnosis for effective treatment. With such consideration, an effective automatic classification of these abnormalities from a video capsule endoscopy (VCE) frame is crucial for improvement in diagnostic workflows. The work presents the process of developing and evaluating a novel model designed to classify gastrointestinal anomalies from a VCE video frame. Integration of Omni Dimensional Gated Attention (OGA) mechanism and Wavelet transformation techniques into the model's architecture allowed the model to focus on the most critical areas in the endoscopy images, reducing noise and irrelevant features. This is particularly advantageous in capsule endoscopy, where images often contain a high degree of variability in texture and color. Wavelet transformations contributed by efficiently capturing spatial and frequency-domain information, improving feature extraction, especially for detecting subtle features from the VCE frames. Furthermore, the features extracted from the Stationary Wavelet Transform and Discrete Wavelet Transform are concatenated channel-wise to capture multiscale features, which are essential for detecting polyps, ulcerations, and bleeding. This approach improves classification accuracy on imbalanced capsule endoscopy datasets. The proposed model achieved 92.76% and 91.19% as training and validation accuracies respectively. At the same time, Training and Validation losses are 0.2057 and 0.2700. The proposed model achieved a Balanced Accuracy of 94.81%, AUC of 87.49%, F1-score of 91.11%, precision of 91.17%, recall of 91.19% and specificity of 98.44%. Additionally, the model's performance is benchmarked against two base models, VGG16 and ResNet50, demonstrating its enhanced ability to identify and classify a range of gastrointestinal abnormalities accurately.

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