Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted
Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/9/1609 |
| _version_ | 1797602742148005888 |
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| author | Ahmed Khalid Ebrahim Mohammed Senan Khalil Al-Wagih Mamoun Mohammad Ali Al-Azzam Ziad Mohammad Alkhraisha |
| author_facet | Ahmed Khalid Ebrahim Mohammed Senan Khalil Al-Wagih Mamoun Mohammad Ali Al-Azzam Ziad Mohammad Alkhraisha |
| author_sort | Ahmed Khalid |
| collection | DOAJ |
| description | Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features and keep the essential features. The first methodology for analyzing x-rays and diagnosing the degree of knee inflammation uses the VGG-19 -FFNN and ResNet-101 -FFNN systems. The second methodology of X-ray analysis and diagnosis of KOA grade by Feed Forward Neural Network (FFNN) is based on the combined features of VGG-19 and ResNet-101 before and after PCA. The third methodology for X-ray analysis and diagnosis of KOA grade by FFNN is based on the fusion features of VGG-19 and handcrafted features, and fusion features of ResNet-101 and handcrafted features. For an OAI dataset with fusion features of VGG-19 and handcrafted features, FFNN obtained an AUC of 99.25%, an accuracy of 99.1%, a sensitivity of 98.81%, a specificity of 100%, and a precision of 98.24%. For the RCU dataset with the fusion features of VGG-19 and the handcrafted features, FFNN obtained an AUC of 99.07%, an accuracy of 98.20%, a sensitivity of 98.16%, a specificity of 99.73%, and a precision of 98.08%. |
| first_indexed | 2024-03-11T04:20:55Z |
| format | Article |
| id | doaj.art-2e0eb47228e94828acea7c31f1a8fe91 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T04:20:55Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-2e0eb47228e94828acea7c31f1a8fe912023-11-17T22:46:01ZengMDPI AGDiagnostics2075-44182023-05-01139160910.3390/diagnostics13091609Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and HandcraftedAhmed Khalid0Ebrahim Mohammed Senan1Khalil Al-Wagih2Mamoun Mohammad Ali Al-Azzam3Ziad Mohammad Alkhraisha4Computer Department, Applied College, Najran University, Najran 66462, Saudi ArabiaDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, YemenDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, YemenComputer Department, Applied College, Najran University, Najran 66462, Saudi ArabiaComputer Department, Applied College, Najran University, Najran 66462, Saudi ArabiaKnee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features and keep the essential features. The first methodology for analyzing x-rays and diagnosing the degree of knee inflammation uses the VGG-19 -FFNN and ResNet-101 -FFNN systems. The second methodology of X-ray analysis and diagnosis of KOA grade by Feed Forward Neural Network (FFNN) is based on the combined features of VGG-19 and ResNet-101 before and after PCA. The third methodology for X-ray analysis and diagnosis of KOA grade by FFNN is based on the fusion features of VGG-19 and handcrafted features, and fusion features of ResNet-101 and handcrafted features. For an OAI dataset with fusion features of VGG-19 and handcrafted features, FFNN obtained an AUC of 99.25%, an accuracy of 99.1%, a sensitivity of 98.81%, a specificity of 100%, and a precision of 98.24%. For the RCU dataset with the fusion features of VGG-19 and the handcrafted features, FFNN obtained an AUC of 99.07%, an accuracy of 98.20%, a sensitivity of 98.16%, a specificity of 99.73%, and a precision of 98.08%.https://www.mdpi.com/2075-4418/13/9/1609deep learningknee osteoarthritisfusion featureshandcrafted featuresFFNN |
| spellingShingle | Ahmed Khalid Ebrahim Mohammed Senan Khalil Al-Wagih Mamoun Mohammad Ali Al-Azzam Ziad Mohammad Alkhraisha Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted Diagnostics deep learning knee osteoarthritis fusion features handcrafted features FFNN |
| title | Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted |
| title_full | Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted |
| title_fullStr | Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted |
| title_full_unstemmed | Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted |
| title_short | Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted |
| title_sort | hybrid techniques of x ray analysis to predict knee osteoarthritis grades based on fusion features of cnn and handcrafted |
| topic | deep learning knee osteoarthritis fusion features handcrafted features FFNN |
| url | https://www.mdpi.com/2075-4418/13/9/1609 |
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