DETECTION OF COVID-19 USING X-RAY IMAGES BY MACHINE LEARNING /
OSAMA NOOR ALDEEN IBRAHIM ODEH; SUPERVISOR: ASST. PROF. DR. Ali IŞIN
- 61 sheets; 31 cm. Includes CD
Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Bioengineering Department
Includes bibliography (sheets 57-61)
ABSTRACT
The COVID-19 epidemic is rapidly spreading over the globe, infecting significant numbers of individuals in a short period. Trends aren't yet apparent, but some studies suggest that this issue will continue through 2024. Machine learning models trained on medical pictures perform better using these methods and approaches. These techniques have been developed over the past two decades to increase the quality of images and improve machine learning models' performance. It has become necessary to use computer-aided diagnosis to reliably and quickly detect coronavirus disease (COVID-19) during a pandemic to alleviate pressure on the healthcare system. Chest X-ray imaging has several advantages over conventional imaging and detection methods. Numerous studies have been conducted on COVID-19 identification from various original X-ray pictures (Rahman et al., 2021). However, no studies have examined image impact on detecting COVID-19 in large datasets. AI can address issues such as a lack of RT-PCR test kits, high test costs, and lengthy wait times for test results. As a result, enhancing an image, it's important not to modify the data in any way of percent accuracy rate. Finally, patients will frequently need to be assessed in short periods by a small number of doctors with limited resources..