The coronavirus, which appeared in Wuhan city of China and named COVID-19 , spread rapidly and caused the death of many people. Early diagnosis is very important to prevent or slow the spread. The first preferred method by clinicians is real-time reverse transcription-polymerase chain reaction (RT-PCR). However, expected accuracy values cannot be obtained in the diagnosis of patients in the incubation period. Therefore, common lung devastation in COVID-19 patients were considered and radiological lung images were used to diagnose. In this study, automatic COVID-19 diagnosis was made from posteroanterior (PA) chest X-Ray images by deep learning method. In the study, using two different deep learning methods, classification was made with different dataset combinations consisting of healthy, COVID, bacterial pneumonia and viral pneumonia X-ray images. The results show that the proposed deep learning-based system can be used in the clinical setting as a supplement to RT-PCR test for early diagnosis
Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.
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