Knee sprain detection and diagnosis using MRI processing and expert rules
Knee sprains are common among people, especially athletes who are involved in sports that require a lot of twisting and jumping like basketball, football and other similar sports. It is a joint injury that causes a stretch or tears in one or more knee ligaments and is graded as Grade I, II or III de...
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oai:animorepository.dlsu.edu.ph:etd_bachelors-117452021-09-22T06:12:39Z Knee sprain detection and diagnosis using MRI processing and expert rules Casimiro, Karen Claire J. Flordeliza, Jhaphet Sheim S. Ong, Jocelyn N. Sese, Jose Alberto G. Knee sprains are common among people, especially athletes who are involved in sports that require a lot of twisting and jumping like basketball, football and other similar sports. It is a joint injury that causes a stretch or tears in one or more knee ligaments and is graded as Grade I, II or III depending on the amount of damage to the ligament. Magnetic Resonance Imaging (MRI) is the most accurate tool used in viewing the knee ligaments. The radiologist will then analyze and diagnose the MRI scan to be presented to the patient and orthopedic. In order to provide a general diagnosis of the knee sprain that is based on an expert's knowledge, and to provide an alternative way to counter check an expert's observation of abnormalities of the knee sprain, the proponents decided to implement a knee sprain detection and diagnosis system. This will be done by having an MRI scan as input, process the image and produce the results of the image, apply these results to the decision rules that are based from experts' data and information and queries from the user, and the system will output the diagnosis for the patient. 2006-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/11100 Bachelor's Theses English Animo Repository Knee--Wounds and injuries Magnetic resonance imaging--Diagnostic use Computer Sciences |
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Knee--Wounds and injuries Magnetic resonance imaging--Diagnostic use Computer Sciences Casimiro, Karen Claire J. Flordeliza, Jhaphet Sheim S. Ong, Jocelyn N. Sese, Jose Alberto G. Knee sprain detection and diagnosis using MRI processing and expert rules |
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Knee sprains are common among people, especially athletes who are involved in sports that require a lot of twisting and jumping like basketball, football and other similar sports. It is a joint injury that causes a stretch or tears in one or more knee ligaments and is graded as Grade I, II or III depending on the amount of damage to the ligament. Magnetic Resonance Imaging (MRI) is the most accurate tool used in viewing the knee ligaments. The radiologist will then analyze and diagnose the MRI scan to be presented to the patient and orthopedic. In order to provide a general diagnosis of the knee sprain that is based on an expert's knowledge, and to provide an alternative way to counter check an expert's observation of abnormalities of the knee sprain, the proponents decided to implement a knee sprain detection and diagnosis system. This will be done by having an MRI scan as input, process the image and produce the results of the image, apply these results to the decision rules that are based from experts' data and information and queries from the user, and the system will output the diagnosis for the patient. |
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text |
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Casimiro, Karen Claire J. Flordeliza, Jhaphet Sheim S. Ong, Jocelyn N. Sese, Jose Alberto G. |
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Casimiro, Karen Claire J. Flordeliza, Jhaphet Sheim S. Ong, Jocelyn N. Sese, Jose Alberto G. |
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Casimiro, Karen Claire J. |
title |
Knee sprain detection and diagnosis using MRI processing and expert rules |
title_short |
Knee sprain detection and diagnosis using MRI processing and expert rules |
title_full |
Knee sprain detection and diagnosis using MRI processing and expert rules |
title_fullStr |
Knee sprain detection and diagnosis using MRI processing and expert rules |
title_full_unstemmed |
Knee sprain detection and diagnosis using MRI processing and expert rules |
title_sort |
knee sprain detection and diagnosis using mri processing and expert rules |
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Animo Repository |
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2006 |
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https://animorepository.dlsu.edu.ph/etd_bachelors/11100 |
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