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Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition

Garv Modwel, Anu Mehra, Nitin Rakesh, K. K. Mishra

Source Title: International Journal of E-Health and Medical Communications (IJEHMC)12(2)

ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799861553|DOI: 10.4018/IJEHMC.2021030106

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MLA

Modwel, Garv, et al. "Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition." IJEHMC vol.12, no.2 2021: pp.93-110. https://doi.org/10.4018/IJEHMC.2021030106

APA

Modwel, G., Mehra, A., Rakesh, N., & Mishra, K. K. (2021). Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition. International Journal of E-Health and Medical Communications (IJEHMC), 12(2), 93-110. https://doi.org/10.4018/IJEHMC.2021030106

Chicago

Modwel, Garv, et al. "Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.2: 93-110. https://doi.org/10.4018/IJEHMC.2021030106

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Abstract

The human vision system is mimicked in the format of videos and images in the area of computer vision. As humans can process their memories, likewise video and images can be processed and perceptive with the help of computer vision technology. There is a broad range of fields that have great speculation and concepts building in the area of application of computer vision, which includes automobile, biomedical, space research, etc. The case study in this manuscript enlightens one about the innovation and future scope possibilities that can start a new era in the biomedical image-processing sector. A pre-surgical investigation can be perused with the help of the proposed technology that will enable the doctors to analyses the situations with deeper insight. There are different types of biomedical imaging such as magnetic resonance imaging (MRI), computerized tomographic (CT) scan, x-ray imaging. The focused arena of the proposed research is x-ray imaging in this subset. As it is always error-prone to do an eyeball check for a human when it comes to the detailing. The same applied to doctors. Subsequently, they need different equipment for related technologies. The methodology proposed in this manuscript analyses the details that may be missed by an expert doctor. The input to the algorithm is the image in the format of x-ray imaging; eventually, the output of the process is a label on the corresponding objects in the test image. The tool used in the process also mimics the human brain neuron system. The proposed method uses a convolutional neural network to decide on the labels on the objects for which it interprets the image. After some pre-processing the x-ray images, the neural network receives the input to achieve an efficient performance. The result analysis is done that gives a considerable performance in terms of confusion factor that is represented in terms of percentage. At the end of the narration of the manuscript, future possibilities are being traces out to the limelight to conduct further research.