ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images

• Published in: Medical engineering & physics Vol. 110; p. 103864
• Main Authors: Key, Sefa, Demir, Sukru, Gurger, Murat, Yilmaz, Erhan, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Arunkumar, N., Tan, Ru-San, Acharya, U Rajendra
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-12-2022
• Subjects: Biceps tendinosis detection; Classification; Deep feature extraction; Humans; Iterative feature selection; Machine learning; Magnetic Resonance Imaging - methods; Neural Networks, Computer; Rotator Cuff Injuries - diagnostic imaging; Rotator cuff tear detection; Shoulder - diagnostic imaging; ViVGG19; Rotator cuff tear detection; Biceps tendinosis detection; Deep feature extraction; Classification; Machine learning; ViVGG19; Iterative feature selection
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2022.103864

•New MR image datasets was collected to diagnose two shoulder ailments.•A patch-based VGG19 has been presented to extract features.•A new image classification model (ViVGG19) has been presented using our feature generator.•Three shallow classifiers have been used to denote high classification ability ViVGG19.•The ViVGG19 attained over 99.5% classification accuracies for the used all datasets.
: Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs. normal and BT vs. normal on MRI images.
: We have developed an exemplar deep feature extraction model to diagnose RCT and BT disorders. The model was tested on a new MR image dataset comprising transverse, sagittal, and coronal MRI images of the shoulder that had been organized into three cases. BT was studied on transverse MRI images (Case 1), while RCT was studied on sagittal (Case 2) and coronal MRI images (Case 3). Our model comprised deep feature generation using a pre-trained VGG19, feature selection using iterative neighborhood component analysis (INCA), and classification using shallow standard classifiers k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). In the feature extraction phase, two fully connected layers were used to extract deep features from the original image, and sixteen fixed-size patches obtained by the division of the original image. This model was named Vision VGG19 (ViVGG), analogous to vision transformers (ViT). The feature vector is extracted from the raw image dataset, and 16 feature vectors are extracted from each fixed-size patch. Seventeen feature vectors obtained from each image are obtained from fc6 and fc7 layers of the pre-trained VGG19, are merged to obtain final feature vector. INCA was used to choose the top features from the created features, and the chosen features were classified using shallow classifiers.
: We defined three cases to evaluate the proposed ViVGG19 to diagnose RT and BCT disorders. Our proposed ViVGG19 model achieved more than 99% accuracy using the KNN classifier.
: ViVGG19 is a very effective model for detecting RCT and BT disorders on shoulder MRI images. The developed automated system is ready to be tested with a bigger diverse database obtained from different medical centers.