Yang, Y., Jin, Y., Tian, Q., Yang, Y., Qin, W., & Ke, X. (2024). Enhancing Gastrointestinal Diagnostics with YOLO-Based Deep Learning Techniques. Preprints. https://doi.org/10.20944/preprints202408.1202.v1
Chicago/Turabian Style
Yang, Y., Weijian Qin and Xiaolan Ke. 2024 "Enhancing Gastrointestinal Diagnostics with YOLO-Based Deep Learning Techniques" Preprints. https://doi.org/10.20944/preprints202408.1202.v1
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
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
