{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T05:19:29Z","timestamp":1773033569871,"version":"3.50.1"},"reference-count":55,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07","funder":[{"name":"Researchers Supporting Project","award":["RSPD2023R704"],"award-info":[{"award-number":["RSPD2023R704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,6,15]]},"abstract":"<jats:p> With the changing lifestyle, a large population suffers from a bone disease known as an osteoarthritis affecting the knee, spine, and hip. Therefore, timely detection and classification of the disease are necessary to minimize the loss, however, it is a time-consuming task and requires various tests and physicians\u2019 in-depth analysis. Thus, an accurate automated technique, timely detection and classification are needed to cope with the aforementioned challenges. This study proposes a technique based on an efficient DenseNet that uses the knee image\u2019 features to identify the Knee Osteoarthritis (KOA) and determine its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III, and Grade-IV. We introduced the reweighted cross-entropy loss function which makes our proposed algorithm more robust as the training data is imbalanced. The dense connections of efficient DenseNet with regularization power help to reduce the overfitting during the training of small knee sample training sets. The proposed algorithm is an efficient approach that can identify the early symptoms of KOA and classify the severity level of the disease for better decision making by orthopedics. The algorithm is a pre-trained network that does not require a huge training set, therefore, the existing dataset i.e. Mendeley VI has been utilized for the training and testing. Additionally, cross-validation has been employed using the OAI dataset to assess the performance of the proposed model. The algorithm achieved 98.22% accuracy over the testing set and 98.08% accuracy over cross-validation. Various experiments have been performed to confirm that our proposed algorithm is more consistent and capable of detecting and classifying the KOA disease than existing state of the art. <\/jats:p>","DOI":"10.1142\/s0218001423520109","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T08:48:23Z","timestamp":1680252503000},"source":"Crossref","is-referenced-by-count":13,"title":["A Robust Framework for Severity Detection of Knee Osteoarthritis Using an Efficient Deep Learning Model"],"prefix":"10.1142","volume":"37","author":[{"given":"Rabbia","family":"Mahum","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology-Taxila, Taxila 47050, Pakistan"}]},{"given":"Aun","family":"Irtaza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology-Taxila, Taxila 47050, Pakistan"}]},{"given":"Mohammed A.","family":"El-Meligy","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, College of Engineering, King Saud University, PO Box 800, Riyadh 11421, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6722-8366","authenticated-orcid":false,"given":"Mohamed","family":"Sharaf","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, College of Engineering, King Saud University, PO Box 800, Riyadh 11421, Saudi Arabia"}]},{"given":"Iskander","family":"Tlili","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Ton University, Da Tan University, Da Nang 550000, Vietnam"}]},{"given":"Saamia","family":"Butt","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, University of Sialkot, Pakistan"}]},{"given":"Asad","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Snt-Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8993-4667","authenticated-orcid":false,"given":"Muhammad","family":"Awais","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, P. R. China"},{"name":"Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou 450002, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"issue":"1","key":"S0218001423520109BIB001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-42215-9","volume":"9","author":"Abedin J.","year":"2019","journal-title":"Sci. Rep."},{"issue":"2","key":"S0218001423520109BIB002","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10462-020-09865-y","volume":"54","author":"Adegun A.","year":"2021","journal-title":"Artif. Intell. Rev."},{"issue":"12","key":"S0218001423520109BIB003","doi-asserted-by":"crossref","first-page":"2939","DOI":"10.3390\/diagnostics12122939","volume":"12","author":"Ahmed S. M.","year":"2022","journal-title":"Diagnostics"},{"key":"S0218001423520109BIB004","first-page":"1725","volume-title":"2019 18th IEEE Int. Conf. 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