A SSLBP-based feature extraction framework to detect bones from knee MRI scans

Mun, Jinyeong; Jang, Youjeong; Yoon, Hyeun Joong; Kim, John

doi:10.1145/3264746.3264778

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Mahrukh et al used a HOG-based template matching automated technique for required region extraction named tibiofemoral in knee radiographs [ 52 ]. Their methodology achieved an accuracy of 96.10% with an average mean rate of 88.26%, which exceeds current strength approaches such as fuzzy-c means and deep models [ 53 ]. A three-dimensional deformation technique for homogeneity in the knee was developed and evaluated.…”

Section: Related Workmentioning

confidence: 86%

“…Extraction of features and location steps are performed by using YOLO-v2 in a single unit. The proposed model YOLO-v2ONNX has 31 layers designed by using YOLO-v2 with the pre-trained architecture of the ONNX [ 52 , 53 , 54 ] model for the detection of KOA. ONNX model is a multiple output network in which 35 layers are present, but this work used only 24 layers for the preparation of the proposed model as (i) input layer, (ii) 2 element-wise Affine layers, (iii) 4 convolutional layers, (iv) 4 BN layers, (v) 3 max-pooling layers, and (vi) 4 activation layers.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Yunus

Amin

Sharif

et al. 2022

Life

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Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.

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Section: Related Workmentioning

confidence: 86%

Section: Proposed Methodologymentioning

confidence: 99%

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Yunus

Amin

Sharif

et al. 2022

Life

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“…Exact knee bone recognition through the proposed model would be an essential aid for the improvement of a completely self-sufficient careful framework. [24] Researchers have no plan to do more research on this topic…”

Section: S7mentioning

confidence: 99%

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

et al. 2020

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The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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