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

Yunus, Usman; Amin, Javeria; Sharif, Muhammad; Yasmin, Mussarat; Kadry, Seifedine; Krishnamoorthy, Sujatha

doi:10.3390/life12081126

Cited by 32 publications

(12 citation statements)

References 75 publications

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“…That study used magnified images focusing upon osteophytes. The diagnosis of knee osteoarthritis using a deep Siamese convolution neural network had average accuracy of 66.7% and kappa coefficient of 0.83 [ 30 ]. Elsewhere, feature value extraction with machine learning has been used in the detection of cancer and COVID-19 using MRI images and radiography [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning

Hida,

Eto,

Wada

et al. 2023

Life

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Hallux valgus, a frequently seen foot deformity, requires early detection to prevent it from becoming more severe. It is a medical economic problem, so a means of quickly distinguishing it would be helpful. We designed and investigated the accuracy of an early version of a tool for screening hallux valgus using machine learning. The tool would ascertain whether patients had hallux valgus by analyzing pictures of their feet. In this study, 507 images of feet were used for machine learning. Image preprocessing was conducted using the comparatively simple pattern A (rescaling, angle adjustment, and trimming) and slightly more complicated pattern B (same, plus vertical flip, binary formatting, and edge emphasis). This study used the VGG16 convolutional neural network. Pattern B machine learning was more accurate than pattern A. In our early model, Pattern A achieved 0.62 for accuracy, 0.56 for precision, 0.94 for recall, and 0.71 for F1 score. As for Pattern B, the scores were 0.79, 0.77, 0.96, and 0.86, respectively. Machine learning was sufficiently accurate to distinguish foot images between feet with hallux valgus and normal feet. With further refinement, this tool could be used for the easy screening of hallux valgus.

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Section: Discussionmentioning

confidence: 99%

Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning

Hida,

Eto,

Wada

et al. 2023

Life

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“…In [ 20 ], 2D radiograph images are converted into 3D images and the LBP features are extracted. Dark-net-53 and Alex-Net were used to extract the deep features and the images were classified with an accuracy of 90.6%.…”

Section: Related Workmentioning

confidence: 99%

Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images

et al. 2023

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One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren–Lawrence (KL) system. This requires the physician’s expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.

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“…Unlike Grid and Random search methods, Bayesian optimization employs previous iterations of the algorithm. This facilitates Bayesian optimization to choose the optimal combination of the hyperparameters for model evaluation [37], [38]. Due to the volume of data involved and the complexity of computations necessary, training deep learning models can be time-consuming.…”

Section: F Bayesian Optimization For Hyperparameter Tuningmentioning

confidence: 99%

Multiple Types of Cancer Classification Using CT/MRI Images Based on Learning Without Forgetting Powered Deep Learning Models

Subramanian¹,

Cho

Sathishkumar

et al. 2023

IEEE Access

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Cancer is the second biggest cause of death worldwide, accounting for one of every six deaths. On the other hand, early detection of the disease significantly improves the chances of survival. The use of Artificial Intelligence (AI) to automate cancer detection might allow us to evaluate more cases in less time. In this research, AI-based deep learning models are proposed to classify the images of eight kinds of cancer, such as lung, brain, breast, and cervical cancer. This work evaluates the deep learning models, namely Convolutional Neural Networks (CNN), against classifying images with cancer traits. Pre-trained CNN variants such as MobileNet, VGGNet, and DenseNet are employed to transfer the knowledge they learned with the ImageNet dataset to detect different kinds of cancer cells. We use Bayesian Optimization to find the suitable values for the hyperparameters. However, transfer learning could make it so that models can no longer classify the datasets they were initially trained. So, we use Learning without Forgetting (LwF), which trains the network using only new task data while keeping the network's original abilities. The results of the experiments show that the proposed models based on transfer learning are more accurate than the current state-of-the-art techniques. We also show that LwF can better classify both new datasets and datasets that have been trained before.

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

Cited by 32 publications

References 75 publications

Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning

Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning

Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images

Multiple Types of Cancer Classification Using CT/MRI Images Based on Learning Without Forgetting Powered Deep Learning Models

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