KNEE OSTEOARTHRITIS DETECTION USING AN IMPROVED CENTERNET WITH PIXEL WISE VOTING SCHEME

Abstract

Radiologists have been using multi-view images to figure out knee problems, such as computer
tomography (CT) scans, MRIs, and X-rays. The most cost-effective approach for obtaining images is Xray,
which is used regularly. There are several image-processing approaches available to detect knee
disease in its early stages. However, the present methods might be enhanced in terms of accuracy and
precision. Furthermore, hand-crafted feature extraction techniques in machine learning-based approaches
are time-consuming. So, The paper proposes a technique based on a customized CenterNet with a pixelwise
voting scheme to automatically extract features for knee disease detection. The proposed model uses
the most representative features and a weighted pixel-wise voting scheme to give a more accurate
bounding box based on the voting score from each pixel inside the former box. The proposed model is a
robust and improved architecture based on CenterNet utilizing a simple DenseNet-201 as a base network
for feature extraction. The proposed model detects knee osteoarthritis (KOA) in knee images precisely
and determines its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III,
and Grade-IV. The proposed technique outperforms existing techniques with an accuracy of 99.14% over
testing and 98.97% over cross-validation