Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection
The fast development of image recognition and information technology has influenced people’s life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the cur...
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| Veröffentlicht in: | Computational Intelligence and Neuroscience Jg. 2022; S. 1 - 8 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Hindawi
30.04.2022
Wiley John Wiley & Sons, Inc |
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| Abstract | The fast development of image recognition and information technology has influenced people’s life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor–patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system. |
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| AbstractList | The fast development of image recognition and information technology has influenced people’s life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor–patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system. The fast development of image recognition and information technology has influenced people's life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor-patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system.The fast development of image recognition and information technology has influenced people's life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor-patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system. |
| Audience | Academic |
| Author | Qi, Xuanye |
| AuthorAffiliation | Department of Design, Graduate School of Design, Kyushu University, Fukuoka 815-0000, Japan |
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| BackLink | https://cir.nii.ac.jp/crid/1871709543063764992$$DView record in CiNii https://www.ncbi.nlm.nih.gov/pubmed/35535190$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3348/kjr.2017.18.4.570 10.1038/s41591-018-0240-2 10.1038/s41573-019-0024-5 10.1186/s13075-021-02526-7 10.1016/j.jcot.2014.07.007 10.1097/j.pain.0000000000002071 10.1146/annurev-bioeng-071516-044442 10.1007/s10514-016-9586-9 10.1016/j.ophtha.2017.02.008 10.1148/radiol.2018171820 10.4018/ijeis.2018070107 10.1109/jiot.2020.3034024 10.1186/s12891-018-2234-0 10.1007/s40595-016-0076-y 10.1302/0301-620x.102b8.bjj-2019-1666.r1 10.1186/s13018-022-02917-9 10.1016/j.jacc.2017.03.571 10.1038/s41568-018-0016-5 10.1016/j.jaim.2021.01.011 10.1001/jamaoncol.2016.2631 10.1007/s13748-019-00203-0 10.1109/tii.2014.2306382 10.1111/aas.13078 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Xuanye Qi. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Xuanye Qi. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Xuanye Qi. 2022 |
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| References | 11 22 12 23 13 14 15 16 17 18 19 1 2 3 4 5 6 7 8 9 20 10 21 37502042 - Comput Intell Neurosci. 2023 Jul 19;2023:9832910. doi: 10.1155/2023/9832910 |
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| Snippet | The fast development of image recognition and information technology has influenced people’s life and industry management mode not only in some common fields... The fast development of image recognition and information technology has influenced people's life and industry management mode not only in some common fields... |
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| SubjectTerms | Algorithms Artificial intelligence Artificial neural networks Back Muscles Classification Cloud computing Computed tomography Computer vision Computers CT imaging Deep learning Developing countries Efficiency Feature extraction Health care industry Health care reform Hospitals Humans Image detection Image processing Image Processing, Computer-Assisted Image Processing, Computer-Assisted - methods Image segmentation Information management Information systems LDCs Lumbar region Machine vision Medical imaging Medical imaging equipment Medical personnel Medicine Muscles Neural networks Neural Networks, Computer Object recognition Patients Physicians R&D Research & development Research Article Research methodology Satellite communications Semantic segmentation Semantics Signs and symptoms Ultrasonic imaging Ultrasound |
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| Title | Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection |
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