@inproceedings{8198fd43a66348ecb2f64a8f396e5c7f,
title = "Developing a Deep Learning Model Using Transfer Learning from EfficientNet-b3 to Detect Knee Fracture on X-ray Images",
abstract = "Conventional radiographs are used for fracture detection routinely in knee injury patients. Miss diagnosis is harmful to patients and stressful to physicians. Thus, a clinical decision support system utilizing a deep neural network should be helpful in preventing physicians from overlooking and also improving patient safety. This study uses a deep learning model (DLM) with transfer learning from EfficientNet-b3 to detect knee fractures on X-ray images. About 12% of the total 13,615 cases were used to test the model. The testing accuracy of the trained model was 90.56%. The area under the receiver operator characteristic curve (AUC) was 0.960. Our findings highlight that the deep learning model can detect knee fractures with remarkable performance. Further implementation into clinical use as a decision support system can be helpful to prevent misdiagnosis and subsequent patient harm.",
keywords = "clinical decision support system, Deep convolutional neural network (DNCC), deep learning model (DLM), Knee fracture, Radiographs, transfer learning, X-ray",
author = "Huang, {Shu Tien} and Liu, {Liong Rung} and Tsai, {Ming Feng} and Huang, {Ming Yuan} and Chiu, {Hung Wen}",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 7th International Conference on Medical and Health Informatics, ICMHI 2023 ; Conference date: 12-05-2023 Through 14-05-2023",
year = "2023",
month = may,
day = "12",
doi = "10.1145/3608298.3608352",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery (ACM)",
pages = "293--296",
booktitle = "ICMHI 2023 - 2023 the 7th International Conference on Medical and Health Informatics",
address = "United States",
}