Computer-aided detection of pulmonary nodules in chest computed tomography images/ Zaid Abduh Hassan Alsaidy ; Supervised Yasser M. Kadah , Manal Abdelwahed ,
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TextLanguage: English Publication details: Cairo : Zaid Abduh Hassan Alsaidy , 2014Description: 78 P. : facsimiles ; 30cmOther title: - {uFEE7}{uFEC8}{uFE8E}م {uئإؤآ}{uئإآ٨}{uئإؤ٢} أورام ا{uئإؤئ}{uئإءإ}{uئإ٨آ}{uئإ٩٤} {uئإؤ٣}{uئإئ٢} {uئإآآ}{uئإإإ}ر ا{uئإئ٧}{uئإآ٧}{uئإأأ}{uئإ٩٤} ا{uئإؤئ}{uئإإ٤}{uئإؤ٨}{uئإأ٤}{uئإأأ}{uئإئ٤}{uئإ٩٤} ا{uئإؤئ}{uئإآأ}{uئإءء}ر{uئإئ٣}{uئإ٩٤} {uئإ٩١}{uئإإ٤}{uئإآ٤}{uئإ٨إ}{uئإأآ}{uئإءء}ة ا{uئإؤئ}{uئإء٤}{uئإ٨إ}{uئإآ٣}{uئإ٩٠} [Added title page title]
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قاعة الرسائل الجامعية - الدور الاول | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.13.03.M.Sc.2014.Za.C (Browse shelf(Opens below)) | Not for loan | 01010110064646000 | ||
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مخـــزن الرســائل الجـــامعية - البدروم | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.13.03.M.Sc.2014.Za.C (Browse shelf(Opens below)) | 64646.CD | Not for loan | 01020110064646000 |
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| Cai01.13.03.M.Sc.2014.Sh.S System of medical Devices Prioritization and Risk-Based Management Using HFMEA / | Cai01.13.03.M.Sc.2014.Sh.S System of medical Devices Prioritization and Risk-Based Management Using HFMEA / | Cai01.13.03.M.Sc.2014.Za.C Computer-aided detection of pulmonary nodules in chest computed tomography images/ | Cai01.13.03.M.Sc.2014.Za.C Computer-aided detection of pulmonary nodules in chest computed tomography images/ | Cai01.13.03.M.Sc.2015.Ah.A Automatic detection of retinal fundus images features for detection of diabetic retinopathy / | Cai01.13.03.M.Sc.2015.Ah.A Automatic detection of retinal fundus images features for detection of diabetic retinopathy / | Cai01.13.03.M.Sc.2015.Ah.I An integrated evaluation for the performance of clinical engineering department / |
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
Lung cancer remains the leading cause of cancer-related deaths in the world. Early diagnosis can improve the effectiveness of treatment and increase the patient{u201F}s chance of survival. Detecting pulmonary nodules in chest computed tomography (CT) scans helps to diagnose lung cancer in an early stage, assess the risk of malignancy. A computer-aided detection (CADe) system is developed by proposing algorithms for classification of lung tissues in chest CT scans to distinguish between nodules (abnormal tissues) and non-nodule (normal tissues) using different combination of 2-D features. The components of the CADe system include feature extraction, feature normalization, feature selection and binary classification (Nodule or Non-Nodule). The system provided excellent results with high sensitivity and specificity on data obtained from two different international reference data sets
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