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Enhancing Gastrointestinal Diagnostics with YOLO-Based Deep Learning Techniques
- 1 Southern Methodist University
- 2 California Institute of Technology
- 3 George Washington University
- 4 George Washington University
- 5 Weill Cornell Medicine
- 6 Harvard University
* Author to whom correspondence should be addressed.
Abstract
Gastrointestinal (GI) tract disorders, ranging from benign polyps to aggressive forms of cancer, pose significant health challenges globally. Early detection and precise classification of these conditions are crucial for effective treatment and improving patient survival rates. This study employs the Hyper-Kvasir dataset, a comprehensive collection of endoscopic images, to develop deep learning models that harness the power of the YOLO (You Only Look Once) architecture for real-time detection and classification of GI abnormalities. The focus is on overcoming inherent challenges such as class imbalance and limited annotated data availability. Advanced machine learning strategies, including data augmentation and semi-supervised learning, are utilized to enhance the model's performance. Our experiments demonstrate notable improvements in the detection of pre-cancerous lesions and other GI abnormalities, confirming the potential of integrating AI into endoscopic practices to support clinicians, reduce diagnostic errors, and contribute to more accurate and timely diagnoses. The implications of these findings are significant, offering a pathway to more reliable diagnostic processes and ultimately, better patient management in gastroenterology.
Keywords
Gastrointestinal Tract Diagnostics, Deep Learning, Hyper-Kvasir Dataset, YOLO
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Cite this article
Yang,Y.;Jin,Y.;Tian,Q.;Yang,Y.;Qin,W.;Ke,X. (2025). Enhancing Gastrointestinal Diagnostics with YOLO-Based Deep Learning Techniques. Theoretical and Natural Science,95,39-46.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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