Machine Learning and Signal Processing Methodologies to Diagnose Human Knee Joint Disorders: A Computational Analysis
- Title
- Machine Learning and Signal Processing Methodologies to Diagnose Human Knee Joint Disorders: A Computational Analysis
- Creator
- Murugan R.; Balajee A.; Senbagamalar L.; Aadil M.; Ganie S.M.
- Description
- Computer-aid diagnostic (CAD) has emerged as a highly innovative research topic in diverse fields which includes medical imaging systems, radiology diagnostics, and so on. These are the systems that majorly assist doctors by the way of interpretation of medical data or images. In the diagnosis of knee joint disorder technique, both time and frequency-based analysis can be done. These non-stationary and non-linear signals are processed into three important methods, namely VMD, TVF-EMD, and CEEMDAN. To analyze the vibroarthrographic (VAG) signal, the initial stage is to compute the mode strategies termed as intrinsic mode functions (IMFs) which can be attained only after performing the transformations. In our chapter, we analyzed Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for computing the mode signals. The CEEMDAN method utilized the time and frequency data for the available features. The feature extraction depends purely on pixel intensity and the statistical parameters. The classification of available data samples is done through the Least Square Support Vector Machine (LS-SVM) and SVM-Recursion of Feature Elimination (SVM-RFE) for the efficient analysis of healthy and unhealthy data samples. 2024 selection and editorial matter, Hemachandran K., Raul V. Rodriguez, Umashankar Subramaniam, and Valentina Emilia Balas; individual chapters, the contributors.
- Source
- Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, pp. 119-130.
- Date
- 2023-01-01
- Publisher
- CRC Press
- Coverage
- Murugan R., Department of CSE, CHRIST (Deemed to be University), India; Balajee A., Department of Computer Science and Engineering, Srinivasa Ramanujan Centre, SASTRA (Deemed to be University), Kumbakonam, India; Senbagamalar L., Department of Information Technology, Karpagam College of Engineering, Coimbatore, India; Aadil M., School of Computer Science and IT, Jain (Deemed to be University), Bangalore, India; Ganie S.M., Department of Analytics, School of Business, Woxsen University, Hyderabad, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-100093459-5; 978-103235416-3
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Murugan R.; Balajee A.; Senbagamalar L.; Aadil M.; Ganie S.M., “Machine Learning and Signal Processing Methodologies to Diagnose Human Knee Joint Disorders: A Computational Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 22, 2025, https://archives.christuniversity.in/items/show/18439.