000			03574nam a22003017a 4500
003			KOHA
005			20230419084605.0
008			221104d2022 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 2455 _bL29 2022
100	1		_aLawal, Misbahu Koramar
245	1	0	_aAPPLICATION OF TRANSFER LEARNING FOR THE DETECTION OF TUBERCULOSIS FROM MICROSCOPIC SLIDE AND CHEST X-RAY IMAGES / _cMISHABU KORAMAR BOKO LAWAL; SUPERVISOR: Asst. Prof. Dr. Kamil YURTKAN
264			_c2022
300			_a46 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 37-40)
520			_aABSTRACT Tuberculosis can be detected from microscopic slides and chest X-ray images. The motivation of the automatic detection systems for tuberculosis is the high cases of tuberculosis. The particular detection problems can be solved by employing an AI-driven model which learn features. In this thesis, an automated detection model is presented using X-ray and microscopic slide images to detect tuberculosis into positive and negative cases. A pretrained Alexnet model is employed. The experiments are done on Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Federal Medical Center Zamfara and Kaggle repository. For classification of tuberculosis using microscopic slide images, the model recorded 90.56 testing accuracy, 97.78% testing sensitivity and 83.33% testing specificity for 70: 30 training and testing splits, 100% testing accuracy, 100% testing sensitivity and 100% testing specificity for 80:20 split, 96.67% testing accuracy, 93.33% testing sensitivity and 100% testing specificity for 90:10 training and testing split and 94.66% average testing accuracy, 98.33% average testing sensitivity and 91.00 % average testing specificity after cross validation. For classification of tuberculosis using X-ray images, the used method achieved 93.89% testing, 96.67% testing sensitivity and 91.11% testing specificity for 70:30 training and testing splits, 94.17% testing accuracy, 98.33% testing sensitivity and 90% testing specificity for 80:20 training and testing split, 98.33% testing accuracy, 100% testing sensitivity and 96.67% testing specificity for 90:10 training and testing split and 94.00% average testing accuracy, 98.33% average testing sensitivity and 98.33% average testing specificity after cross validation. Our results are following the notion that pretrained convolutional neural network models can take part for categorizing medical images with acceptable accuracy. These particular models can be used as a confirmatory tool for diagnosis of both pneumonia and tuberculosis, augmenting miss diagnosis and bring an option for the higher and monotonous workload faced by specialists, radiologists and pathologists in Federal Medical Centre Zamfara. Keywords: Chest X-ray, deep learning, microscopic slide, pretrained network, AlexNet, Tuberculosis, transfer learning.
650		0	_a Deep learning (Machine learning) _vDissertations, Academic
650		0	_aTuberculosis _vDissertations, Academic
650		0	_aTransfer learning (Machine learning) _vDissertations, Academic
700	1		_aYurtkan, Kamil _esupervisor
942			_2ddc _cTS
999			_c289011 _d289011