May 10, 2022 · A robust, handcrafted feature extraction technique that is presented for the classification of COVID-19 detection using the Lung X-Ray dataset.
May 10, 2022 · The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.
Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The ...
Mar 13, 2023 · Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia.
Missing: histogram- | Show results with:histogram-
Mar 13, 2023 · Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three ...
Missing: histogram- oriented
Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The ...
The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) ...
This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN)
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Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The ...
Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The ...