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

Mahum, Rabbia; Rehman, Saeed Ur; Meraj, Talha; Rauf, Hafiz Tayyab; Irtaza, Aun; El-Sherbeeny, Ahmed M.; El-Meligy, Mohammed A.

doi:10.3390/s21186189

Cited by 56 publications

(26 citation statements)

References 54 publications

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“…Tibiofemoral joint was detected by Mahum et al [40] using matching technique with the knee image database. HOG was used to compute the similarity among the image blocks pixel by pixel, where the pixels with maximum similarity were chosen as region of interest.…”

Section: Detection Of Knee Jointmentioning

confidence: 99%

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Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

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Section: Detection Of Knee Jointmentioning

confidence: 99%

“…Mahum et al [40] used hybrid feature descriptors, CNN with HOG, and CNN with LBP to extract meaningful features from radiographs. ree classifiers, support vector machine, random forest, and K nearest neighbour, were employed and compared.…”

Section: Classification Of Knee Oa Severitymentioning

confidence: 99%

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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“…Deep learning and deep features [44] are extensively applied in many other applications [45][46][47][48]. By looking into their robustness in different aspects of complex problem solutions, the proposed methodology also applied them.…”

Section: Machine Learning-based Classificationmentioning

confidence: 99%

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

et al. 2022

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Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.

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“…There are some recent works that have uses features fusion in other medical imaging domains [ 17 , 22 , 25 ] that urges the proposed study to use the features fusion in its work. However, the recent workings on deep features also enhancing the other aspect of field such as knee Osteoarthritis [ 21 ]. The recent work regarding ML [ 6 ] and DL [ 30 ] based approaches on COVID-19 detection also motivated to use some different sort of strategy.…”

Section: Related Workmentioning

confidence: 99%

AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification

Albahli

Meraj

Chakraborty

et al. 2022

Multimed Tools Appl

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To identify various pneumonia types, a gap of 15% value is being created every five years. To fill this gap, accurate detection of chest disease is required in the healthcare department to avoid any serious issues in the future. Testing the affected lungs to detect a Coronavirus 2019 (COVID-19) using the same imaging modalities may detect some other chest diseases. This wrong diagnosis strongly needs a multidisciplinary approach to the right diagnosis of chest-related diseases. Only a few works till now are targeting pathological x-ray images. Many studies target only a single chest disease that is not enough to automate chest disease detection. Only a few studies regarding the observation of the COVID-19, but more cases are those where it can be misclassified as detecting techniques not providing any generic solution for all types of chest diseases. However, the existing studies can only detect if the person has COVID-19 or not. The proposed work significantly contributes to detecting COVID-19 and other chest diseases by providing useful analysis of chest-related diseases. One of our testing approaches achieves 90.22% accuracy for 15 types of chest disease with 100% correct classification of COVID-19. Though it analyzes the perfect detection as the accuracy level is high enough, but it would be an excellent decision to consider the proposed study until doctors can visually inspect the input images used by models that lead to its detection.

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

Cited by 56 publications

References 54 publications

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification

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