A systematic approach to diagnosis and categorization of bone fractures in X-Ray imagery

ABSTRACT

Researchers have proposed several ways of diagnosing, classifying and categorizing bone fractures. Nevertheless, no standardized classification has yet been established for all identified fractures. In recent times Machine learning and deep learning are becoming more popular. Deep Neural Networks (DNN) are well-known models for their image classification capabilities and capacity to tackle complex problems. To categorize (normal, comminute, oblique, spiral, greenstick, impacted, and transverse) and recognize fragmented images, the -Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) feature extraction approaches were utilized for several algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Inception V3 and ResNeXt101. This research aims to construct an image processing system, including information from X-ray and Computer Tomography (CT) scans, classifying bone fractures rapidly and precisely. Pre-processing, quality enhancement, and extraction techniques are utilized to process X-ray images of the shattered bone collected from the hospital. The images are then separated and classified into fractured and unfractured bones and are compared with the accuracy of other methodologies like KNN, SVM, RF, InceptionV3, and ResNeXt101. According to the findings, the bone fracture detection and classification system perform well-using ResNeXt101, with an accuracy rate of 93.75%.

KEYWORDS:

• Convolutional neural network
• deep learning
• edge detection
• bone fracture classification
• image segmentation
• machine learning

Disclosure statement

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

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

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 a Professor at the Department of IT, 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, information retrievals, cyber security, and machine learning.

S. Chakravarty

Dr S. Chakravarty is a Professor at the Department of CSE, Centurion University of Technology and Management, Odisha, India. She has published many journals in reputed journals. Her research interest includes NLP, Computer vision, Machine learning, Deep Learning, and Image processing.