Detection of COVID-19 Cases with Fuzzy Classifiers Using Chest Computed Tomography

Abstract

The novel coronavirus 2019 (COVID-19) is still spreading rapidly since it first appeared in Wuhan city of China in December 2019, resulting in a worldwide pandemic. Early detection of positive cases plays a key role in preventing the further spread of the epidemic which leads to the development of diagnostic methods that give rapid and accurate responses for the detection of COVID-19. Previous studies confirmed that chest computed tomography (CT) is an indispensable tool for early screening and diagnosing of COVID-19 cases. As a result of examinations on CT scans, a radiological finding that is called ground-glass opacity, causing color, and texture change, was found in the lung of a person with COVID-19. Due to the carelessness of radiologists who work long hours and the misdiagnosis resulting in confusion of the findings with different diseases, an automatic system that helps radiologists is needed. In this paper, we present a new approach based on fuzzy classification for the detection of COVID-19 using 3D CT volumes. In the proposed approach, the skewness, kurtosis, and average statistical features of 3D CT images of patients consisting of two classes, COVID and Normal, are calculated and the value ranges are determined for both classes. Three statistical features and value ranges are used as membership functions in the development of fuzzy logic classifier. The proposed approach provides rapid and accurate diagnostics in terms of COVID vs. Normal (binary classification) under a user-friendly interface. Experimental evaluations demonstrate that our approach has great potential for radiologists to validate their initial screening and improve early diagnosis, isolation, and treatment, which contributes to infection prevention and control of the epidemic.

Beyin Bilgisayarlı Tomografi Görüntülerinde Yapay Zeka Tabanlı Beyin Damar Hastalıkları Tespiti

“…Recent advances in computer processing lead more AI methodologies for big data processing. In that context, deep learning, a branch AI that is capable of extracting features automatically for the classification, regression, segmentation and prediction, has been employed in many applications, including image processing (Gölcez, Kiliç, & Şen, 2021;Volkan Kılıç, Mercan, Tetik, Kap, & Horzum, 2022), video processing (Aydın, Çaylı, Kılıç, & Onan, 2022), speech recognition (Volkan Kılıç, Barnard, Wang, & Kittler, 2013), medical image analysis Kökten & Kılıç, 2021;Şen et al, 2022), computer vision (Volkan Kılıç, 2021, face recognition , advanced vehicle driving assist (Betül, Çaylı, Kılıç, & Onan, 2022), audio analysis (Keskin, Çaylı, Moral, Kılıç, & Onan, 2021), object detection (Liu et al, 2020) and natural language processing (Fetiler, Çaylı, Moral, Kılıç, & Onan, 2021). In addition, deep learning architectures include CNNs , Recurrent Neural Networks (Mercan, Doğan, & Kılıç, 2020;Palaz, Doğan, & Kılıç, 2021), transformers (Sun et al, 2022), and autoencoders (Sewak, Sahay, & Rathore, 2020).…”

Beyin Bilgisayarlı Tomografi Görüntülerinde Derin Öğrenme Tabanlı İskemik İnme Hastalığı Segmentasyonu

“…For feature extraction, deep learning uses network architectures, such as convolutional neural networks (CNNs) (Ağralı et al;Akosman, Öktem, Moral, & Kılıç, 2021;Çaylı, Kılıç, Onan, & Wang, 2022;Keskin, Moral, Kılıç, & Onan, 2021;B. Kilic, Dogan, Kilic, & Kahyaoglu, 2022;Sayraci, Agrali, & Kilic, 2023;Şen et al, 2022;Yüzer, Doğan, Kılıç, & Şen, 2022), reinforcement learning (Agrali, Soydemir, Gökçen, & Sahin, 2021), and recurrent neural networks (RNNs) (Aydın, Çaylı, Kılıç, & Onan, 2022;Fetiler, Caylı, Moral, Kılıc, & Onan, 2021;Gölcez, Kiliç, & Şen, 2019;Keskin, Çaylı, Moral, Kılıc, & Onan, 2021;Kılıc, 2021;Volkan Kılıç;Kökten & Kılıç, 2021;Mercan, Doğan, & Kılıç, 2020;Mercan & Kılıç, 2021;Palaz, Doğan, & Kılıç, 2021). Among these architectures, CNN offers remarkable performance on ischemic stroke disease segmentation.…”