COVID-19 outbreak first emerged on December 31, 2019 in Wuhan, China. The Novel Coronavirus Disease is caused by the SAR-CoV-2 virus, which causes respiratory symptoms such as fever, cough, and shortness of breath. While scientists continue their fight against SARS-CoV-2 (2019-nCoV), one of the deadliest viruses in the last century, with tests to help diagnosis and prognosis, drug and vaccine discovery, Information Technologies mostly continues to work on early diagnosis, prognosis and prediction. The aim is to reveal systems with low margin of error that will alleviate the workload of healthcare professionals, as well as early diagnosis and initiation of treatment.Deep Learning and Computer vision is the most commonly used. Two class (covid, noncovid) classification solution, using the Artificial Intelligence Techniques, have been examined in this paper. CNN architecture, has been created to develop an model to disease detection process of COVID-19(2019-nCoV) virus infected patients from CT images consisting of NON-COVID and COVID classes. We have proposed the classifying of CT images using the 2 Convolutions and pool layers with the model which shortening the time for classification and achieved an accuracy of nearly 94.21%. Results show that the used model attains provide highly satisfying results and can be used for any image classification.
Imaging is needed in stroke cases in order to understand what the type of stroke (ischemic, hemorrhagic) is, to rule out bleeding, to determine the infarct area and to plan treatment. Noncontrast CT is the primary imaging protocol used in the initial evaluation of patients with suspected stroke. As apart from studies in the literature, this paper proposes novel automated classification and segmentation approaches which are capable of extracting hemorrhage and ischemic lesions (infarcts) simultaneously from the noncontrasts brain CT images during the treatment of brain stroke patients. It is aimed to automate the detection of stroke lesions with a high accuracy rate using the U‐Net model for segmentation. In the experiments performed on the real data set, a precision value of 95.06% is obtained for the classification model. For segmentation, the IoU coefficient values from the experiments are 92.01% for hemorrhagic and 82.22% for ischemic, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.