Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN
Knee osteoarthritis is a significant cause of physical inactivity and disability. Early detection and treatment of osteoarthritis (OA) degeneration can decrease its course. Physicians’ scores may differ significantly amongst interpreters and are greatly influenced by personal experience based solely...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1658 |
| _version_ | 1797625087069782016 |
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| author | Ganesh Kumar M Agam Das Goswami |
| author_facet | Ganesh Kumar M Agam Das Goswami |
| author_sort | Ganesh Kumar M |
| collection | DOAJ |
| description | Knee osteoarthritis is a significant cause of physical inactivity and disability. Early detection and treatment of osteoarthritis (OA) degeneration can decrease its course. Physicians’ scores may differ significantly amongst interpreters and are greatly influenced by personal experience based solely on visual assessment. Deep convolutional neural networks (CNN) in conjunction with the Kellgren–Lawrence (KL) grading system are used to assess the severity of OA in the knee. Recent research applied for knee osteoarthritis using machine learning and deep learning results are not encouraging. One of the major reasons for this was that the images taken are not pre-processed in the correct way. Hence, feature extraction using deep learning was not great, thus impacting the overall performance of the model. Image sharpening, a type of image filtering, was required to improve image clarity due to noise in knee X-ray images. The assessment used baseline X-ray images from the Osteoarthritis Initiative (OAI). On enhanced images acquired utilizing the image sharpening process, we achieved a mean accuracy of 91.03%, an improvement of 19.03% over the earlier accuracy of 72% by using the original knee X-ray images for the detection of OA with five gradings. The image sharpening method is used to advance knee joint recognition and knee KL grading. |
| first_indexed | 2024-03-11T09:51:52Z |
| format | Article |
| id | doaj.art-3dd6e764b09f482fa7e67baf045f3a9d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:51:52Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3dd6e764b09f482fa7e67baf045f3a9d2023-11-16T16:08:39ZengMDPI AGApplied Sciences2076-34172023-01-01133165810.3390/app13031658Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNNGanesh Kumar M0Agam Das Goswami1School of Electronics Engineering, VIT-AP University, G-30, Inavolu, Beside AP Secretariat, Amaravati 522237, Andhra Pradesh, IndiaSchool of Electronics Engineering, VIT-AP University, G-30, Inavolu, Beside AP Secretariat, Amaravati 522237, Andhra Pradesh, IndiaKnee osteoarthritis is a significant cause of physical inactivity and disability. Early detection and treatment of osteoarthritis (OA) degeneration can decrease its course. Physicians’ scores may differ significantly amongst interpreters and are greatly influenced by personal experience based solely on visual assessment. Deep convolutional neural networks (CNN) in conjunction with the Kellgren–Lawrence (KL) grading system are used to assess the severity of OA in the knee. Recent research applied for knee osteoarthritis using machine learning and deep learning results are not encouraging. One of the major reasons for this was that the images taken are not pre-processed in the correct way. Hence, feature extraction using deep learning was not great, thus impacting the overall performance of the model. Image sharpening, a type of image filtering, was required to improve image clarity due to noise in knee X-ray images. The assessment used baseline X-ray images from the Osteoarthritis Initiative (OAI). On enhanced images acquired utilizing the image sharpening process, we achieved a mean accuracy of 91.03%, an improvement of 19.03% over the earlier accuracy of 72% by using the original knee X-ray images for the detection of OA with five gradings. The image sharpening method is used to advance knee joint recognition and knee KL grading.https://www.mdpi.com/2076-3417/13/3/1658knee osteoarthritisknee-X-ray imagesInception ResNetV2image sharpening |
| spellingShingle | Ganesh Kumar M Agam Das Goswami Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN Applied Sciences knee osteoarthritis knee-X-ray images Inception ResNetV2 image sharpening |
| title | Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN |
| title_full | Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN |
| title_fullStr | Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN |
| title_full_unstemmed | Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN |
| title_short | Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN |
| title_sort | automatic classification of the severity of knee osteoarthritis using enhanced image sharpening and cnn |
| topic | knee osteoarthritis knee-X-ray images Inception ResNetV2 image sharpening |
| url | https://www.mdpi.com/2076-3417/13/3/1658 |
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