ACL TEAR DIAGNOSIS FROM KNEE MRI USING CNN AND RNN ARCHITECTURE

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dc.contributor.authorOMER SAIF
dc.date.accessioned2025-09-26T16:31:59Z
dc.date.available2025-09-26T16:31:59Z
dc.date.issued2019
dc.description.abstractAccurate diagnosis always has been the basis of the best suitable treatment for the patient. As far as knee ligament injuries are concerned MRI is utmost preferred method for identification of exact abnormality. However, radiologists working on interpretations is time consuming, tiring and may be wedded to errors. By using computer aided diagnosis such errors can be omitted quite efficiently and will also serve its purpose of correctly identifying the pathology. Deep learning method has been introduced in which layers of features have been embedded which is suitable for the mingled relations that exist between the images and the results. DL methodologies have outperformed conventional methods of image analysis and empowered huge advancement in therapeutic imaging assignments. Following study has been conducted to detect ACL tear by using CNN-RNN method in knee MRI. This will make its mark in identifying the patients at risk and serves its best in clinical decision making. By automatizing the process and by using CNN techniques for feature extraction and LSTM for unique decision support for detection of tears. ACL tears can be detected either partial or complete. Thus, increasing specificity and sensitivity of MRI in knee related ligamentous injuries.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/7186
dc.language.isoen
dc.publisherUMT, Lahore
dc.titleACL TEAR DIAGNOSIS FROM KNEE MRI USING CNN AND RNN ARCHITECTURE
dc.typeThesis
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