000			02599nam a22003257a 4500
003			KOHA
005			20230419105638.0
008			230217d2022 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 2692 _bO44 2022
100	1		_aOdeh, Osama Noor Aldeen Ibrahim
245	1	0	_a DETECTION OF COVID-19 USING X-RAY IMAGES BY MACHINE LEARNING / _cOSAMA NOOR ALDEEN IBRAHIM ODEH; SUPERVISOR: ASST. PROF. DR. Ali IŞIN
264			_c2022
300			_a61 sheets; _c31 cm. _eIncludes CD
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Bioengineering Department
504			_aIncludes bibliography (sheets 57-61)
520			_aABSTRACT 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..
650		0	_aArtificial intelligence _vDissertations, Academic
650		0	_aCovid-19 (Diseases) _vDissertations, Academic
650		0	_aX-rays _vDissertations, Academic
650		0	_aMachine learning _vDissertations, Academic
650		0	_aDeep learning (Machine learning) _vDissertations, Academic
700	1		_aIşın, Ali _esupervisor
942			_2ddc _cTS
999			_c289782 _d289782