Medical image reasoning with the convolutional neural network - based fuzzy logic

The new type of coronavirus (Covid-19), first detected in a local wild animal market in China's Hubei province in December 2019, has turned into a pandemic that has affected the whole world in a year. A definitive treatment for the disease has not yet been found. Although the vaccine development and applications have yielded positive results, the epidemic has not yet been brought under control worldwide. The most effective method in stopping the epidemic is the rapid detection of cases and the implementation of the quarantine measures together with the vaccination. The use of Convolutional Neural Netwoks(CNN), which has successful applications in the field of Computer Vison(CV) in recent years, as an auxiliary method for radiologists in the diagnosis of pandemic cases is the subject of this thesis. Within the scope of the study, the Covid-19 diagnostic methods are examined. The structure of the CNN is examined and its usage in image classification is explained. Different CNN architectures and development environments are introduced. Biomedical imaging techniques used in the diagnosis of the pandemics are briefly introduced. Preprocessing and data augmentation methods for image sets to be used in the CNN applications are discussed. The generated CNN model was trained and tested with Computed Tomohraphy(CT) chest images obtained from open sources. Test results were compared with studies in the literature. Fuzzy logic was applied to interpret the classification success of a specific sample with the trained network.