Meniscus tears are serious knee abnormalities that can cause knee osteoarthritis disorder. Therefore, early detection and treatment of meniscus tears that may occur in the knee with computer-aided systems will prevent the progression of these disorders. In this study, an approach which can detect the meniscus tears automatically by using and comparing two different feature extraction methods have been presented. With these methods, features of the knee MR images were obtained and automatic meniscus tear classification was performed by such features. Four different classifiers have been used to model the features in the classification phase. The most successful classification results were obtained from the support vector machines (SVM) with a success rate of 90.13% and the extreme learning machines (ELM) with a success rate of 87.85% via the LBP feature extraction method. It is observed that better results are obtained than the ones in similar studies in the literature. It is aimed to improve the existing success with the use of deep feature extraction methods in the future.
Meniscus tears are serious knee abnormalities that can cause knee osteoarthritis disorder. Therefore, early detection and treatment of meniscus tears that may occur in the knee with computer-aided systems will prevent the progression of these disorders. In this study, an approach which can detect the meniscus tears automatically by using and comparing two different feature extraction methods have been presented. With these methods, features of the knee MR images were obtained and automatic meniscus tear classification was performed by such features. Four different classifiers have been used to model the features in the classification phase. The most successful classification results were obtained from the support vector machines (SVM) with a success rate of 90.13% and the extreme learning machines (ELM) with a success rate of 87.85% via the LBP feature extraction method. It is observed that better results are obtained than the ones in similar studies in the literature. It is aimed to improve the existing success with the use of deep feature extraction methods in the future.
| Birincil Dil | en |
|---|---|
| Konular | Bilgisayar Bilimleri, Bilgi Sistemleri |
| Bölüm | Makaleler |
| Yazarlar |
|
| Destekleyen Kurum | TÜBİTAK |
| Proje Numarası | 116E151 |
| Teşekkür | The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2- 2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners. This study was supported by Turkish Scientific and Technical Research Council-TÜBİTAK (Project Number: 116E151). |
| Tarihler |
Yayımlanma Tarihi : 31 Aralık 2019 |
| Bibtex | @araştırma makalesi { bilmes609750,
journal = {International Scientific and Vocational Studies Journal},
issn = {2618-5938},
address = {Gaziosmanpaşa Üni. Taşlıçiftlik kampüsü Teknopark binası No:111 Tokat/Merkez},
publisher = {Umut SARAY},
year = {2019},
volume = {3},
pages = {92 - 104},
doi = {},
title = {Automated Diagnosis of Meniscus Tears from MRI of the Knee},
key = {cite},
author = {Saygılı, Ahmet and Varlı, Songül}
} |
| APA | Saygılı, A , Varlı, S . (2019). Automated Diagnosis of Meniscus Tears from MRI of the Knee . International Scientific and Vocational Studies Journal , 3 (2) , 92-104 . Retrieved from https://dergipark.org.tr/tr/pub/bilmes/issue/51590/609750 |
| MLA | Saygılı, A , Varlı, S . "Automated Diagnosis of Meniscus Tears from MRI of the Knee" . International Scientific and Vocational Studies Journal 3 (2019 ): 92-104 <https://dergipark.org.tr/tr/pub/bilmes/issue/51590/609750> |
| Chicago | Saygılı, A , Varlı, S . "Automated Diagnosis of Meniscus Tears from MRI of the Knee". International Scientific and Vocational Studies Journal 3 (2019 ): 92-104 |
| RIS | TY - JOUR T1 - Automated Diagnosis of Meniscus Tears from MRI of the Knee AU - Ahmet Saygılı , Songül Varlı Y1 - 2019 PY - 2019 N1 - DO - T2 - International Scientific and Vocational Studies Journal JF - Journal JO - JOR SP - 92 EP - 104 VL - 3 IS - 2 SN - 2618-5938- M3 - UR - Y2 - 2019 ER - |
| EndNote | %0 International Scientific and Vocational Studies Journal Automated Diagnosis of Meniscus Tears from MRI of the Knee %A Ahmet Saygılı , Songül Varlı %T Automated Diagnosis of Meniscus Tears from MRI of the Knee %D 2019 %J International Scientific and Vocational Studies Journal %P 2618-5938- %V 3 %N 2 %R %U |
| ISNAD | Saygılı, Ahmet , Varlı, Songül . "Automated Diagnosis of Meniscus Tears from MRI of the Knee". International Scientific and Vocational Studies Journal 3 / 2 (Aralık 2019): 92-104 . |
| AMA | Saygılı A , Varlı S . Automated Diagnosis of Meniscus Tears from MRI of the Knee. BİLMES DERGİ, ISVOS. 2019; 3(2): 92-104. |
| Vancouver | Saygılı A , Varlı S . Automated Diagnosis of Meniscus Tears from MRI of the Knee. International Scientific and Vocational Studies Journal. 2019; 3(2): 92-104. |
| IEEE | A. Saygılı ve S. Varlı , "Automated Diagnosis of Meniscus Tears from MRI of the Knee", International Scientific and Vocational Studies Journal, c. 3, sayı. 2, ss. 92-104, Ara. 2020 |