000			02187cam a2200313 a 4500
003			EG-GiCUC
008			211128s2021 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aPh.D
099			_aCai01.13.03.Ph.D.2021.Ay.A
100	0		_aAyat Motawakkel Karrar Ahmed
245	1	2	_aA 3D reconstruction of pulmonary nodules from 2D CT images computer aided diagnosis based system / _cAyat Motawakkel Karrar Ahmed ; Supervised Manal Abdelwahed , Mai Said Mabrouk
246	1	5	_aبناء أورام الرئة ثلاثية الأبعاد من الصور المقطعية ثنائية الأبعاد اعتمادا على نظام تشخيص الحاسب الآلى
260			_aCairo : _bAyat Motawakkel Karrar Ahmed , _c2021
300			_a102 P. : _bcharts , facsimiles ; _c30cm
502			_aThesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
520			_aLung cancer is one of the most serious cancers in the world with the minimum survival rate. Lung nodules may be isolated from (solitary) or attached to (juxtapleural) other. In this paper a Computer Aided Diagnosis system is proposed to classify between solitary nodule and juxtapleural nodule inside the lungs.Two main auto-diagnostic schemes of supervised learning for classification are achieved.Three segmentation approaches are proposed.The three classifiers of the first scheme are K-Nearest Neighborhood, Artificial Neural Network and Support Vector Machine. In the second scheme, Deep Convolutional neural networksare used. Because of limited data sample and imbalanced data, 10-fold cross validation and random oversampling are used.The 3D reconstruction of pulmonary nodules based on the surface rendering technique and visualization by 3D slicer are achieved
530			_aIssued also as CD
653		4	_a3D Reconstruction
653		4	_aDeep learning
653		4	_aMaximum Intensity Projection
700	0		_aMai Said Mabrouk , _eSupervisor
700	0		_aManal Abdelwahed , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c83323 _d83323