Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
Series Vol. 2 , 22 March 2023
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Deep Learning Approach for Predicting Bone Disorder Using DenseNet
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Abstract
Osteoporosis is a medical condition that affects the structure and strength of bones. Osteoporosis is an asymptomatic disease of the bone that affects a significant proportion of the world's elderly, leading to increased fragility of the bone and an increased risk of fracture. This paper's key objective is to provide a critical review of the main artificial intelligence-based systems for detecting populations at risk of osteoporosis or fractures. Skeletal deformities, fractures, twisted knees, inherited bone defects, and other bone disorders affect millions of individuals as a result of a variety of bone disorders. These may help to prevent a variety of possible complications if diagnosed and treated early. We discussed deep neural networks in this paper, including recognition, segmentation, and classification. The architecture and concepts of the deep learning algorithm we used to detect bone density were also discussed. As a result, we’ll use a variety of deep learning algorithms to build a model that can detect a person's bone mass density and recognize any potential threats that have occurred or could occur.
Keywords
Machine Learning, Convolution Neural Network, Bone Mass Density, Musculoskeletal Radiographs.
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
- ISBN (Print)
- 978-1-915371-19-5
- ISBN (Online)
- 978-1-915371-20-1
- Published Date
- 22 March 2023
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/2/20220599
- Copyright
- 22 March 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited