Noname manuscript No. (will be inserted by the editor) Efficiency of Artificial Intelligence in Detecting COVID-19 Pneumonia and Other Pneumonia Causes by Quantum Fourier Transform Method Erdi Acar · Bilge ¨ Oztoprak · Mustafa Re¸ sorlu · Murat Da¸ s · ˙ Ihsan Yılmaz · ˙ Ibrahim ¨ Oztoprak Received: date / Accepted: date Abstract The new coronavirus (COVID-19) appeared in Wuhan in December 2019 and has been announced as a pandemic by the World Health Organiza- tion (WHO). Currently, this deadly pandemic has caused more than 1 million deaths worldwide. Therefore, it is essential to detect positive cases as early as possible to prevent the further spread of this outbreak. Currently, the most widely used COVID-19 detection technique is a real-time reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR is time-consuming to confirm in- fection in the patient. Because RT-PCR is less sensitive, it provides high false- negative results. Computed tomography (CT) is recommended as a solution to this problem by healthcare professionals because of its higher sensitivity for early and rapid diagnosis. In addition, radiation used in CT poses a serious threat to patients. In this study, we propose a CNN-based method to distinguish COVID- 19 pneumonia from other types of viral and bacterial pneumonia using low-dose CT images to reduce the radiation dose used in CT. In our study, we used a data set consisting of 7717 CT images of 350 patients that we collected from C ¸ anakkale Onsekiz Mart University Research Hospital. We used a CNN-based network that suppresses noise to remove interference from low-dose CT images. In the image preprocessing phase, we provided lung segmentation from CT images and applied quantum Fourier transform. By evaluating all possible variations of local knowl- edge at the same time with quantum Fourier transformation, the most informative spatial information was extracted. In CNN-based architecture, we used pre-trained ResNet50v2 as a feature extractor and fine-tune by training with our dataset. We visualized the efficiency of the ResNet50v2 network using the t-SNE method. We performed the classification process with a fully connected layer. We created a heat map using the GradCam technique to see where the model focuses on the images Erdi Acar Dept. of Computer Engineering, Institute of Science, C ¸anakkale Onsekiz Mart University, C ¸anakkale, Turkey E-mail: erdiacar@stu.comu.edu.tr Bilge ¨ Oztoprak Dept. of Radiology, Faculty of Medicine, C ¸anakkale Onsekiz Mart University, C ¸ anakkale, Turkey E-mail: bilgeoztoprak@gmail.com . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted January 4, 2021. ; https://doi.org/10.1101/2020.12.29.20248900 doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.