Author: Öztürk, Şaban; Özkaya, Umut; Barstuğan, Mücahid
Title: Classification of Coronavirus (COVIDâ€19) from Xâ€ray and CT images using shrunken features Cord-id: cl2d401h Document date: 2020_8_18
ID: cl2d401h
Snippet: Necessary screenings must be performed to control the spread of the COVIDâ€19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVIDâ€19. The information obtained by using Xâ€ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it i
Document: Necessary screenings must be performed to control the spread of the COVIDâ€19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVIDâ€19. The information obtained by using Xâ€ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it is aimed to develop a machine learning method for the detection of viral epidemics by analyzing Xâ€ray and CT images. In this study, images belonging to six situations, including coronavirus images, are classified using a twoâ€stage data enhancement approach. Since the number of images in the dataset is deficient and unbalanced, a shallow image augmentation approach was used in the first phase. It is more convenient to analyze these images with handâ€crafted feature extraction methods because the dataset newly created is still insufficient to train a deep architecture. Therefore, the Synthetic minority overâ€sampling technique algorithm is the second data enhancement step of this study. Finally, the feature vector is reduced in size by using a stacked autoâ€encoder and principal component analysis methods to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially to make the diagnosis of COVIDâ€19 in a short time and effectively. Also, it is thought to be a source of inspiration for future studies for deficient and unbalanced datasets.
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