Architecture of the proposed deep learning model for hip disease screening.

<p>The model consists of an EfficientNet-B3 backbone for feature extraction, followed by a nonlocal block for capturing long-range dependencies. The extracted features are then processed through global average pooling, a fully connected layer, and a sigmoid activation for binary classification...

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Bibliographic Details
Main Author:	Jung-Wee Park (10689240) (author)
Other Authors:	Seung Min Ryu (15810611) (author), Hong-Seok Kim (1535005) (author), Young-Kyun Lee (5917250) (author), Jeong Joon Yoo (17607426) (author)
Published:	2025
Subjects:	Biochemistry Medicine Biotechnology Marine Biology Biological Sciences not elsewhere classified potentially aiding physicians positive predictive value four different models first model depended designed &# 8211 based screening model external validation set 783 ), validation distinguishing normal hips 8 &# 177 severe hip diseases screening hip diseases hip disease group 0 &# 177 deep learning models internal test set plain hip radiographs npv ), f1 hip diseases normal group deep learning test set 196 ), npv 0 plain radiographs 8 years 726 radiographs 226 radiographs ppv 0 auroc 0 xlink "> unrelated marks study aimed sciatic notch raw image preprocessed images preprocessed image mean age gradcam image femur shaft backbone algorithm 98 ). 9 years
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Architecture of the proposed deep learning model for hip disease screening.

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