According to the data of 2020, it is seen that 1 of every eight cancers diagnosed worldwide and the 5th among cancers that cause death is breast cancer. Cancer can spread to different organs and reach an incurable stage in patients who are not diagnosed and treated at the right time. Therefore, reducing the time taken for breast cancer diagnosis and reducing mortality rates are of great importance for accurate and early diagnosis of the disease. This study aims to improve the accuracy of cancer detection by using various machine learning algorithms and methods for artificial intelligence-based breast cancer diagnosis. By using ultrasonography images taken from 780 people, image information processed with statistical parameters was extracted. Artificial intelligence-based breast cancer detection was performed by applying three different machine learning algorithms and the hybrid machine learning algorithm designed as a combination of these algorithms on the extracted data set. In this way, early detection of cancerous cells will be carried out without creating advanced risks for the individual, and treatment will be possible.
Purpose: The present study aims to compare the results of the COVID-19 rapid antigen test (ExacTest™ COVID-19 Antigen Rapid Test) and real-time polymerase chain reaction (RT-PCR) test in samples of people suspected of coronavirus disease (COVID-19). Materials and Methods: Among the samples submitted between January 2022 and March 2022 with suspicion of COVID-19, 299 samples subject to simultaneous COVID-19 RADT (Rapid Antigen Detection Test) and RT-PCR were evaluated retrospectively. The Real-Time PCR test was studied with the DS CORONEX COVID-19 Multiplex Real time-qPCR Test Kit (DS Nano and Biotechnology Product Tracing and Tracking Co., Turkey) and the rapid antigen test was studied by the immunochromatographic method with ExacTest™ COVID-19 Antigen Rapid Test Cassette kit (General Diagnostica inc., California, USA). Ag-RDT test results were evaluated with the fluorescent immunoassay analyzer (FIATEST Analyzer, Hangzhou Alltest Biotech Co., Ltd. China). Results: RT-PCR test was positive in 53 (17.7%) samples. The RADT's sensitivity was found 88.7 (95% Cl 77.0-95.7), specificity 98.0 (95% Cl 95.3-99.3), positive predictive value 90.4 (95% Cl 79.7-95.8), negative predictive value 97.6 (95% Cl 95.0-98.8), and accuracy 96.3 (95% Cl 93.5-98.2). Sample sensitivities of patients under and over 18 years of age have been identified as 75 (95% Cl 19.4-99.4) and 89.8 (95% Cl 77.8-96.6), respectively. The sensitivity of patients with and without symptoms was 95.5 (95% Cl 77.2-99.9) and 83.9 (95% Cl 66.3-94.6), respectively. For samples with a cycle threshold (Ct) of
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