To overcome this limitation, the proposed Multi-Tier Rank-based Semi-supervised deep learning (MTR-SDL) for Shoulder X-Ray Classification uses the small labelled dataset to generate a labelled dataset from unable dataset to obtain performance equivalent to approaches trained on the enormous dataset. ...
Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train...
The models are then trained on a small labelled data set of X-ray images of shoulder implants. The SSP shows excellent results in five ImageNet models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, and EfficientNet with precision of 96.69%, 95.45%, 98.76%, 98.35%, and ...
Images were labelled by two raters, and inferior–superior glenohumeral translation was calculated. During abduction, glenohumeral translation (mean (standard deviation)) ranged from 3.3 (2.2) mm for 0 kg to 4.1 (1.8) mm for 4 kg, and from 2.3 (1.5) mm for 0 kg to 3.8 (2.2) mm for ...