Computer Science > Computer Vision and Pattern Recognition
arXiv:2410.19363 (cs)
[Submitted on 25 Oct 2024 (v1), last revised 23 Dec 2024 (this version, v2)]
Title:Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations
View a PDF of the paper titled Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations, by Lakshmi Srinivas Panchananam and 3 other authors
View PDFHTML (experimental)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.
| Comments: | Capsule Vision 2024 Challenge |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2410.19363 [cs.CV] |
| (orarXiv:2410.19363v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2410.19363 arXiv-issued DOI via DataCite |
Submission history
From: Lakshmi Srinivas Panchananam [view email][v1] Fri, 25 Oct 2024 08:01:35 UTC (958 KB)
[v2] Mon, 23 Dec 2024 04:32:45 UTC (958 KB)
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View a PDF of the paper titled Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations, by Lakshmi Srinivas Panchananam and 3 other authors
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