A New Approach to Classify Knee Osteoarthritis Severity from Radiographic Images based on CNN-LSTM Method

Wahyuningrum, Rima Tri; Anifah, Lilik; Purnama, I Ketut Eddy; Purnomo, Mauridhi Hery

doi:10.1109/icawst.2019.8923284

Cited by 37 publications

(14 citation statements)

References 20 publications

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“…CNNs have been used for various image classification tasks, with recent studies developing CNN models for medical image analysis. The early work of using CNNs to classify knee OA was mainly applied to radiographic (X-ray) images [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…This method used branches in its CNN that are referred to as "attention modules", which provide an unsupervised determination of the ROI of X-ray images. Another recent work added a long short-term memory (LSTM) classifying step following the CNN layers in their network [18]. Given the nature of LSTM for processing sequential data, additional images were generated in a preprocessing step by cropping a fixed ROI and rotating the cropped image by 5, 10, −5, and −10 degrees.…”

Section: Introductionmentioning

confidence: 99%

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Knee Osteoarthritis Classification Using 3D CNN and MRI

2021

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Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0–4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL ≤ 1) from OA (KL ≥ 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knee Osteoarthritis Classification Using 3D CNN and MRI

2021

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“…The VGG-19 based technique has been used in [ 55 ], achieving an accuracy of 69.70% using the OAI dataset. CNN and LSTM based knee severity disease classification was performed in [ 56 ] using the OAI dataset and attaining 75.28% accuracy. The maximum accuracy is attained by [ 57 ] based on the LSVM classifier.…”

Section: Experimental Evaluationmentioning

confidence: 99%

A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis

Mahum

Rehman

Meraj

et al. 2021

Sensors

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In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren–Lawrence (KL) system. The Kellgren–Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL.

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“…Different architectures of deep learning have been applied in different types of medical images from imaging modalities such as radiography, ultrasound, computed tomography, and MRI to diagnose knee OA. Among all the deep learning architectures, CNN architecture has gained a large amount of research interest, particularly in knee OA segmentations and diagnosis [ 26 , 28 , 29 ]. One of the main advantages of CNN is that they are easier to train and have fewer parameters compared to other architectures [ 30 ].…”

Section: Imaging-based Deep Learningmentioning

confidence: 99%

“…Numerous studies utilize knee X-ray plain radiography in their classification model not only because it is commonly available and cost-efficient but also because the most significant hallmarks of OA are JSN and osteophyte formation which can be easily visualized by knee X-rays. Moreover, the JSN plays an important role in determining OA severity according to the KL-grade, which is a relatively commonly used grading by practitioners worldwide [ 16 , 29 , 59 ]. The KL-grading system (as shown in Figure 5 ) is categorized into five grades based on the ground truth where Grade 0 indicates no OA, Grade 1 indicates doubtful OA with minute osteophytes, Grade 2 indicates mild OA with definite osteophytes, Grade 3 indicates moderate OA with definite JSN and multiple osteophytes with possible bone deformation, and Grade 4 indicates severe OA where large osteophytes, JSN, severe sclerosis, and definite bone deformity are present [ 29 , 34 ].…”

Section: Application Of 2d Deep Learning In Knee Osteoarthritis Assessmentmentioning

confidence: 99%

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Yeoh

Lai

Goh

et al. 2021

Computational Intelligence and Neuroscience

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Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.

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A New Approach to Classify Knee Osteoarthritis Severity from Radiographic Images based on CNN-LSTM Method

Cited by 37 publications

References 20 publications

Knee Osteoarthritis Classification Using 3D CNN and MRI

Knee Osteoarthritis Classification Using 3D CNN and MRI

A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

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