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An enhanced deep learning approach for cancer diagnosis / Sarah Ali Abdelaziz Ismael ; Supervised Hesham A. Hefny , Ammar Mohammed

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Sarah Ali Abdelaziz Ismael , 2020Description: 87 P . : charts , facsmilies ; 30cmOther title:
  • أسلوب مجود فى التعليم العميق لتشخيص السرطان [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science Summary: Cancer 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
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2020.Sa.E (Browse shelf(Opens below)) Not for loan 01010110082487000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2020.Sa.E (Browse shelf(Opens below)) 82487.CD Not for loan 01020110082487000

Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science

Cancer 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

Issued also as CD

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