Knee Osteoarthritis SCAENet: Adaptive Knee Osteoarthritis Severity Assessment Using Spatial Separable Convolution with Attention-Based Ensemble Networks with Hybrid Optimization Strategy.
Osteoarthritis (OA) of the knee is a chronic state that significantly lowers the quality of life for its patients. Early detection and lifetime monitoring of the progression of OA are necessary for preventive therapy. In the course of therapy, the Kellgren and Lawrence (KL) assessment model categori...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1563 - 1581 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280540&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280540 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280540 185280540 185280540 10.1007/s10278-024-01306-4 185280540 ppf: 1563 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Knee Osteoarthritis SCAENet: Adaptive Knee Osteoarthritis Severity Assessment Using Spatial Separable Convolution with Attention-Based Ensemble Networks with Hybrid Optimization Strategy. aug: au: Devarapaga, Sriramulu Thumma, Rajesh affil: Department of Electronics and Communication Engineering, Anurag University, 500088, Hyderabad, Telangana, India sug: subj: Prediction Models Osteoarthritis, Knee Severity of Illness Evaluation Image Interpretation, Computer Assisted Deep Learning Convolutional Neural Networks Human Validation Studies Experimental Studies ROC Curve Descriptive Statistics ab: Osteoarthritis (OA) of the knee is a chronic state that significantly lowers the quality of life for its patients. Early detection and lifetime monitoring of the progression of OA are necessary for preventive therapy. In the course of therapy, the Kellgren and Lawrence (KL) assessment model categorizes the rigidity of OA. Deep techniques have recently been used to increase the precision and effectiveness of OA severity assessments. The training process is compromised by low-confidence samples, which are less accurate than normal ones. In this work, a deep learning-based knee osteoarthritis severity assessment model is recommended to accurately identify the condition in patients. The phases of the designed model are data collection, feature extraction, and prediction. At first, the images are generally gathered from online resources. The gathered images are given into the feature extraction phase. A new model is implemented to predict knee osteoarthritis named Spatial Separable Convolution with Attention-based Ensemble Networks (SCAENet), which includes feature extraction, stacked target-based feature pool generation, and knee osteoarthritis prediction. The feature extraction is done using ResNet, Visual Geometry Group (VGG16), and DenseNet. The stacked target-based feature pool is obtained from the SCAENet. Hence, the stacked target-based feature pool is obtained by the Hybridization of Equilibrium Slime Mould with Bald Eagle Search Optimization (HESM-BESO). Here, the knee osteoarthritis's severity prediction is performed using the dimensional convolutional neural network (1DCNN) technique. The designed SCAENet model is validated with other conventional methods to show high performance. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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