A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images. (5th September 2022)

• Record Type: Journal Article
• Authors: Sathish Kumar, L.; Prabu, A. V.; Pandimurugan, V.; Rajasoundaran, S.; Malla, Prince Priya; Routray, Sidheswar
• Is Part Of: Concurrency and computation. Volume 34:Number 26(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-05
• Subjects: bone fracture detection -- convolutional networks -- deep learning -- medical imaging -- radiology; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7307
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: X‐ray images are widely used to identify fractures in human bones. Radiographic diagnosis models take more time and manual procedures for establishing physical analysis of bone fractures. Furthermore, the lack of clinical resources and the medical support systems lead to inaccurate bone data extractions. In this case, the need for detail is required to understand the scientific issues in x‐ray and magnetic resonance imaging (MRI) based bone fracture diagnosis solutions. Particularly, the current bone fracture detection models are emerging with computerized frameworks and health informatics consoles. In this regard, this article lists five phases. First, data preparation and data collection tasks are initiated. Second, bone fracture diagnosis models are comparatively analyzed to find optimal observations. Consequently, the third phase of the article examines the treatments against bone fracture observations with traditional and deep learning (DL) techniques. The fourth phase consists of relative diagnosis solutions and the treatment benefits on various types of technical frameworks. Finally, this work concludes the comparison with recent DL‐based bone fracture identification models.