A statistical model approach based on the Gaussian Mixture Model for the diagnosis and classification of bone fractures

ABSTRACT

Medical imaging has significantly influenced the field of image processing in recent years. Several advancements have been made in medical imaging that is now being used, and all of them are geared at making disease diagnosis more accurate. Researchers recently explored how to diagnose, define, and classify bone fractures, and numerous approaches have been proposed. However, a uniform categorization has yet to be developed for all detected fractures. This article focuses on the methods for identifying realignments among the bone structures utilizing model-based approaches, and this methodology is highlighted here. The performance metrics are used to assess the findings, and the constructed model has shown substantial results in binary classification and multi-classification of bone fractures. The model that has been suggested is called the Finite Beta Gaussian Mixture Model (FBGMM), and its performance may be evaluated with the use of a confusion matrix. This project intends to construct an image processing system that can identify bone fractures promptly and reliably by including data from X-rays. FGBMM achieves good accuracy in both binary and multiclassification of fractures.

KEYWORDS:

• CNN
• machine learning
• computer vision
• bone fracture classification
• musculoskeletal images
• deep learning
• Gaussian Mixture Model (GMM)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Santoshachandra Rao Karanam

Santoshachandra Rao Karanam is a Research Scholar at the Department of CSE, Centurion University of Technology and Management, Odisha, India. His research interest includes Machine learning, Deep Learning, Image processing, and problem-solving.

Y. Srinivas

Dr Y.Srinivas is presently working as a Professor in the Department of Computer Science and Engineering, at GITAM University, Visakhapatnam, India. He has guided over 25 research scholars and published many research articles in reputed journals. His research area includes Image Processing and Analysis, Data Mining, Software Engineering, Digital Forensics, Cyber Security, and Machine Learning.

S. Chakravarty

Dr S. Chakravarty, Senior Member of IEEE, is currently working as Dean SoET, Professor of Computer Science & Engineering and Coordinator of the Data Science and Machine Learning Research Centre, CUTM, Odisha. There are about 120 Publications, eight patents published, one patent granted, one book and 25 book chapters to her credit. Recently three of her articles have been recognized by WHO and are being listed on the WHO website. Her research interests are Bio-medical engineering, Smart Agriculture, Intrusion Detection, Image Processing, Hyperspectral and Multispectral Imaging.