Authors :
D.Kiruthika; J. Judith
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3WwRFFc
DOI :
https://doi.org/10.5281/zenodo.7490772
Abstract :
Knee Osteoarthritis (OA) is an extremely
common and degenerative musculoskeletal disease
worldwide which creates a significant burden on patients
with reduced quality of life and also on society because
of its financial impact. Therefore, technical try and
efforts to reduce the burden of the disease could help
both patients and society. In this paper, an automated
novel method is proposed with a supported combination
of joint shape and modified Fully connected neural
network (FCNN) based bone texture features, to
differentiate between the knee radiographs with and
without osteoarthritis. Moreover, an endeavor is formed
to explain the bone texture using CNN. Knee
radiographs from Osteoarthritis Initiative (OAI) and
Multicenter Osteoarthritis (MOST) datasets are utilized
in this paper. The proposed models were trained on 8000
knee radiographs from OAI and evaluated on 3500 knee
radiographs from MOST. The results demonstrate that
fusing the proposed shape and texture parameters
achieves the state-of-the
Keywords :
Knee Osteoarthritis, KL grades, Automatic Detection, Fully Convolutional Neural Networks, Classification and Regression.
Knee Osteoarthritis (OA) is an extremely
common and degenerative musculoskeletal disease
worldwide which creates a significant burden on patients
with reduced quality of life and also on society because
of its financial impact. Therefore, technical try and
efforts to reduce the burden of the disease could help
both patients and society. In this paper, an automated
novel method is proposed with a supported combination
of joint shape and modified Fully connected neural
network (FCNN) based bone texture features, to
differentiate between the knee radiographs with and
without osteoarthritis. Moreover, an endeavor is formed
to explain the bone texture using CNN. Knee
radiographs from Osteoarthritis Initiative (OAI) and
Multicenter Osteoarthritis (MOST) datasets are utilized
in this paper. The proposed models were trained on 8000
knee radiographs from OAI and evaluated on 3500 knee
radiographs from MOST. The results demonstrate that
fusing the proposed shape and texture parameters
achieves the state-of-the
Keywords :
Knee Osteoarthritis, KL grades, Automatic Detection, Fully Convolutional Neural Networks, Classification and Regression.
