A Survey of Deep Learning Techniques Based on Computed Tomography Images for Detection of Pneumonia †

A cluster of cases caused by the virus SARS-CoV-2 was detected in Wuhan, China, in December 2019. The disease derived from that virus was named Coronavirus (COVID-19), which was officially recognized as a pandemic by the World Health Organization in March 2020. Since COVID-19 can cause serious pneumonia, early diagnosis is crucial for adequate treatment and for reducing health system overload. Therefore, deep learning algorithms to detect pneumonia have been developed using computed tomography (CT) scans, as they provide more detailed information about the disease because of their three-dimensionality and good visibility. This information analyzed by specialists could support the confirmation of pneumonia. To find out the accuracy levels of various classifiers, we evaluated the baseline models utilized by researchers. The findings we drew were that the majority of CT classification algorithms have strong accuracy values in comparison to other algorithms performed using CT, but have not reached above 98%. According to the systematic literature survey, low accuracy levels resulting from the performance of the models were attributed to the incongruous dealing of medical images. These images instead of having common formats such as png or jpg, use more complex formats such as DICOM and NIFTI, in order to save more information about the disease and the patient. Moreover, some studies found that the influence of environmental conditions and lung movement could affect the quality of the image. This unclear pneumonia area may also result in a decrease in the efficiency of deep-learning algorithms for detecting pneumonia. Therefore, the objective of this survey is to identify, gather data and build a catalog of deep-learning techniques for detecting pneumonia abnormalities and annotating CT images from the literature review, reflecting a better understanding of the classification of pneumonia using CT images.