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Classification Of Retinal Disorders Using Optical Coherence Tomography Images Based On Medical Expert Systems / Ahmed Mohamed Salaheldin Mohamed ; Supervised Manal Abdelwahed

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ahmed Mohamed Salaheldin Mohamed , 2022Description: 71 P . : charts , facsmilies ; 30cmOther title:
  • تصن{u٠٦أأ}ف اضطرابات الشبك{u٠٦أأ}ة باستخدام صور الاشعة المقطع{u٠٦أأ}ة للشبك{u٠٦أأ}ة عن طر{u٠٦أأ}ق نظم الخبرة الطب{u٠٦أأ}ة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: Vision impairment is increasing at an alarming rate. Diagnosis and classification of retinal disorders is a significant challenge in ophthalmological applications. The thesis aims to classify the optical coherence tomography images into four classes: Choroidal Neovascularization, Diabetic Macular Edema, Drusen, and normal cases. The thesis proposed a robust method based on both machine learning and deep learning approaches. Deep learning-based platform has been proposed using two novel techniques; InceptionV3 and SqueezeNet convolutional neural networks to classify the data and a hybrid machine-deep learning platform using Support Vector Machine (SVM), K-nearest neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) has been proposed also to solve the same problem with another method. The proposed models are presented as a medical expert system that classifies the optical coherence tomography images into the main retinal disorders. The thesis introduces nine evaluation criteria for performance computation
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2022.Ah.C (Browse shelf(Opens below)) Not for loan 01010110085600000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2022.Ah.C (Browse shelf(Opens below)) 85600.CD Not for loan 01020110085600000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

Vision impairment is increasing at an alarming rate. Diagnosis and classification of retinal disorders is a significant challenge in ophthalmological applications. The thesis aims to classify the optical coherence tomography images into four classes: Choroidal Neovascularization, Diabetic Macular Edema, Drusen, and normal cases. The thesis proposed a robust method based on both machine learning and deep learning approaches. Deep learning-based platform has been proposed using two novel techniques; InceptionV3 and SqueezeNet convolutional neural networks to classify the data and a hybrid machine-deep learning platform using Support Vector Machine (SVM), K-nearest neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) has been proposed also to solve the same problem with another method. The proposed models are presented as a medical expert system that classifies the optical coherence tomography images into the main retinal disorders. The thesis introduces nine evaluation criteria for performance computation

Issued also as CD

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