@inproceedings{ca00e0e5a5ce40b88b0ac836b92eef29,
title = "An Image Enhancement Method for Deep Learning-Based Rib Fracture Detection",
abstract = "Fracture is common in clinical medicine, and doctors usually make the initial diagnosis based on X-ray images. Compared to general fractures, diagnosing rib fractures requires more experienced doctors, and the manual diagnosis method requires much time and effort. A computer-aided diagnosis (CAD) system for rib fractures would help reduce the pressure on doctors and the likelihood of missed diagnoses. However, the rib is a circular three-dimensional structure, so there are problems with overlapping ribs, unclear bone features, and edge features in X-ray images. To solve the challenges, this paper proposes a rib X-ray image enhancement method to enrich bone feature information and enhance bone edge information effectively. According to experimental results, the method in this paper can effectively improve the AP by 36.1%.",
keywords = "Computer Aided Diagnosis (CAD), Deep Learning, Image Enhancement, Rib Fractures, X-ray Image",
author = "Chan, {Hung Tse} and Liu, {Yi Hung} and Tarng, {Yih Wen} and Lin, {Ching Han} and Wu, {Man Lin} and Chen, {Yung Yao}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",
year = "2023",
doi = "10.1109/ICCE-Taiwan58799.2023.10227008",
language = "English",
series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "417--418",
booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
address = "United States",
}