Author(s):
1. Haider Masood:
National University of Sciences and Technology Islamabad,Pakistan
2. Eisha Hassan:
National University of Sciences and Technology Islamabad,Pakistan
3. Anum Abdul Salam:
National University of Sciences and Technology Islamabad,Pakistan
4. Muwahida Liaquat:
National University of Sciences and Technology Islamabad,Pakistan
Abstract:
Various deep learning frameworks are being proposed for autonomous detection of diseases to contribute towards telemedicine. Moreover, in spite of low doctor to patient ratio, such algorithms aid physicians in tracking the disease with more accuracy. According to WHO, Osteoarthritis has been declared as the most common form of arthritis. Additionally, it is one of the major reasons of physical disability among older age. Different deep learning framework-based approaches exist for evaluation of Knee osteoarthritis but none of them incorporate the feedback or symptoms of the patients. We have proposed a tri-weightage classification model i.e. a hybrid approach for grading osteoarthritis using structural features from X-Ray images, KOOS questionnaire and flexion angle. Moreover, we conducted a comparison of various deep learning model on our dataset and achieved the highest accuracy of 89.29% for RESNET152V2 and INCEPTIONRESNETV2.
Page(s):
1-1
DOI:
DOI not available
Published:
Journal: IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2) May 24-26, 2022 (Book of Abstracts), Volume: 1, Issue: 1, Year: 2022
Keywords:
deep learning
,
Osteoarthritis
,
OsteoDoc
References:
References are not available for this document.
Citations
Citations are not available for this document.