000			03008cam a2200313 a 4500
003			EG-GiCUC
008			210112s2020 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.02.M.Sc.2020.Sa.E
100	0		_aSarah Ali Abdelaziz Ismael
245	1	3	_aAn enhanced deep learning approach for cancer diagnosis / _cSarah Ali Abdelaziz Ismael ; Supervised Hesham A. Hefny , Ammar Mohammed
246	1	5	_aأسلوب مجود فى التعليم العميق لتشخيص السرطان
260			_aCairo : _bSarah Ali Abdelaziz Ismael , _c2020
300			_a87 P . : _bcharts , facsmilies ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science
520			_aCancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help saving the patient{u2019}s life. Human diagnosis based on MRI images is an unreliable, error-prone, time-consuming, highly specialized task that depends on the experience of the radiologist. Misdiagnosis of brain tumor type can prevent the effective response to medical treatment and decrease the chance of survival among patients. Therefore, there is a high need in the Artificial Intelligence field for a highly accurate Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Traditional machine learning approaches required feature extraction which is a manual and time-consuming task that requires prior knowledge about the problem domain. Over recent years, deep learning has shown an optimistic performance in computer vision systems in the medical imaging domain In this thesis, we propose an enhanced approach for classifying brain tumor types using Residual Networks. The proposed model is evaluated on a benchmark dataset containing 3064 MRI images of 3 brain tumor types. We have achieved the highest accuracy of 99% outperforming the other previous work. The results were evaluated under several performance metrics such as accuracy, precision, recall, f1-score, and balanced accuracy. Furthermore, the results have been evaluated on two different distributions of the dataset, an image-level approach, where images were split randomly into training and validation sets. And a patientlevel approach where a patient is present in either in the training set or validation set but not both
530			_aIssued also as CD
653		4	_aArtificial Neural Network
653		4	_aConvolutional Neural Network
653		4	_aMachine Learning
700	0		_aAmmar Mohammed , _eSupervisor
700	0		_aHesham A. Hefny , _eSupervisor
905			_aAmira _eCataloger
905			_aNazla _eRevisor
942			_2ddc _cTH
999			_c79539 _d79539