Detection and Classification Knee Osteoarthritis Algorithm using YOLOv3 and VGG-16 Models

Huu, Phat Nguyen; Thanh, Dat Nguyen; Hai, Thanh Le Thi; Duc, Hoang Chu; Viet, Hung Pham; Trong, Cac Nguyen

doi:10.1109/atigb56486.2022.9984096

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…This allows the image quality to be improved by increasing the dispersion of the highest frequency and decreasing the dispersion of other frequencies, allowing the low contrast of the source images to be improved [27]. In this work, we have applied the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm used in [21,25]. Then, to improve the performance of the osteoarthritis detection system, it was necessary to use images with variable contrast, brightness, and positions, as they can be captured by the camera used in our work.…”

Section: Preprocessingmentioning

confidence: 99%

“…Huu et al [21] applied the transfer learning of VGG16 for the automated binary classification of KOA severity using a deep Siamese convolution neural network. The proposed model consists of six convolutional layers with a stride of 1, three convolutional layers with a stride of 2, three dropout layers, a Separable Adaptive Max-pooling (SAM) layer, and a fully connected layer.…”

Section: Introductionmentioning

confidence: 99%

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MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis

Touahema,

Zaimi,

Zrira

et al. 2024

Diagnostics

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In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called “MedKnee” is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist’s diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee’s satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.

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Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%