000			02572nam a22002777a 4500
003			KOHA
005			20230713094136.0
008			230713d2023 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 2897 _bV25 2023
100	1		_aVaki, Tafadzwanashe Blessings
245	1	0	_aMALARIA DETECTION USING MACHINE LEARNING / _cTAFADZWANASHE BLESSINGS VAKI; SUPERVISOR: ASST. PROF. DR. EMRE ÖZBİLGE
264			_c2023
300			_aix, 54 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 Computer Engineering Department
504			_aIncludes bibliography (sheets 50-54)
520			_aABSTRACT Malaria is a very serious and sometimes a deadly sickness resulting from a parasite that is commonly found in female anopheles' mosquitos which feed on human beings. Humans that contract malaria are usually very sick with flu-like contamination, very high fevers, and shaking chills. The highest rates of malaria infections are found in African countries. This research was done to provide a cost effective machine learning method to perform malaria diagnosis in Africa especially in the rural areas. The research of this study was carried out using the following methodology. 27,558 images containing equals parts of infected and uninfected blood cells then were further divided into test, training and validation sets. The model was created using convolutional neural networks(CNN) which part of deep learning. Five layers of convolution were used with max pooling layers between the convolution layers. Binary crossentropy was used to calculate the loses. To evaluate the custom CNN model, the metrics measured were accuracy, f1 score, precision and recall. The results of the research showed that the proposed custom CNN model had 96.1% accuracy, an F1 score 0.96, precision score 0.968 and a recall score of 0.95. These results indicated a successful custom CNN implementation. The conclusions and future recommendations are also discussed in this research to further improve accuracy and help the fight against malaria in the rural areas of Africa. Keywords: CNN, Deep learning, Machine learning, Malaria
650		0	_aMalaria _vDissertations, Academic
650		0	_a Machine learning _vDissertations, Academic
700	1		_aÖzbilge, Emre _esupervisor
942			_2ddc _cTS
999			_c290575 _d290575