Abstract
In today’s digital world, the major working culture is shifted to electronic devices such as computers and smartphone causing minimal physical exercise. Due to this reason, at old age people suffer many kinds of health issues such as Osteoarthritis (OA) disease. OA is the most common chronic condition of joints, which is also called as degenerative arthritis or degenerative joint disease. To identify this disease level an automated detection and classification method is required. This also requires expert knowledge person in this area. In this regard some other support is also required which is related to expert person of this area. The current strategy for OA identification includes clinical investigation and medical imaging techniques. In this paper, we detect and classify OA disease in knee from medical images using deep features. As we know, in medical imaging noise has a major role and therefore in this paper we focus on denoising medical images for accurate detection and classification. This paper also focuses on handling huge amount of image data by utilizing some High Performance Computing (HPC). The dataset used in this paper for detection and classification is a keen MRI image. Thus, an integrated discussion of various detection techniques regarding OA is done in a scientific way.
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Acknowledgement
The authors of this paper is thankful to National Institute of Health and Invectus Innovation Pvt. Ltd., Noida to provide medical datasets for evaluating and analyzing it. Authors are also thankful to CIT kokrajhar for utilizing NVIDIA GPU server to carry over this work.
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Singh, P.P., Prasad, S., Chaudhary, A.K., Patel, C.K., Debnath, M. (2020). Classification of Effusion and Cartilage Erosion Affects in Osteoarthritis Knee MRI Images Using Deep Learning Model. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_34
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DOI: https://doi.org/10.1007/978-981-15-4018-9_34
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