Document Details

Document Type : Thesis 
Document Title :
Breast Cancer Detection and Classification Using Advanced Computer-Aided Diagnosis System
كشف وتصنيف سرطان الثدي باستخدام المساعد الحاسوبي التشخيصي
 
Subject : Faculty of Engineering 
Document Language : Arabic 
Abstract : Computer-Aided Diagnosis (CAD) systems are becoming very helpful and useful in supporting physicians for early detection of breast cancer. In this thesis, a CAD system that is able to detect abnormal clusters in mammographic images will be implemented using different classifiers and features. The CAD system will utilize a Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as classifiers. Adopting mammographic database from Mammographic Image Analysis Society (Mini-MIAS), for training and testing, the performance of the two types of classifiers are compared in terms of sensitivity, specificity, and accuracy. The obtained values for the previous parameters show the efficiency of the CAD system to be used as a secondary screening method in detecting abnormal clusters given the Region of Interest (ROI). The best classifier is found to be SVM showed 96% accuracy, 92% sensitivity and 100% specificity. 
Supervisor : Dr. Abdulhameed Alkhateeb 
Thesis Type : Master Thesis 
Publishing Year : 1442 AH
2020 AD 
Co-Supervisor : Dr. Umar S. Alqasemi 
Added Date : Saturday, November 14, 2020 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
يحيى محمد عثمانOsman, Yahia MohammedResearcherMaster 

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 46778.pdf pdf 

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