{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:23:20Z","timestamp":1790789000454,"version":"4.1.0"},"reference-count":58,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP- 2021\/133"],"award-info":[{"award-number":["RSP- 2021\/133"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013Lawrence (KL) system. The Kellgren\u2013Lawrence 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.<\/jats:p>","DOI":"10.3390\/s21186189","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T21:47:11Z","timestamp":1631742431000},"page":"6189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1983-8201","authenticated-orcid":false,"given":"Rabbia","family":"Mahum","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology Taxila, Punjab 47050, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad-Wah Campus, Wah Cantt 47040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5743-3697","authenticated-orcid":false,"given":"Talha","family":"Meraj","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad-Wah Campus, Wah Cantt 47040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1515-3187","authenticated-orcid":false,"given":"Hafiz Tayyab","family":"Rauf","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Engineering & Informatics, University of Bradford, Bradford BD7 1DP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aun","family":"Irtaza\u00a0","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology Taxila, Punjab 47050, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3559-6249","authenticated-orcid":false,"given":"Ahmed M.","family":"El-Sherbeeny","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7967-4259","authenticated-orcid":false,"given":"Mohammed A.","family":"El-Meligy\u00a0","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1093\/rheumatology\/kem250","article-title":"MRI of rheumatoid arthritis\u2014Image quantitation for the assessment of disease activity, progression and response to therapy","volume":"47","author":"Hodgson","year":"2008","journal-title":"Rheumatology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1007\/s11760-020-01645-z","article-title":"X-ray image analysis for automated knee osteoarthritis detection","volume":"14","author":"Saleem","year":"2020","journal-title":"Signal Image Video Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5761","DOI":"10.1038\/s41598-019-42215-9","article-title":"Predicting knee osteoarthritis severity: Comparative modeling based on patient\u2019s data and plain X-ray images","volume":"9","author":"Abedin","year":"2019","journal-title":"Sci. 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