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Deanship of Graduate Studies
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 Type
Dr Grade
Email
يحيى محمد عثمان
Osman, Yahia Mohammed
Researcher
Master
Files
File Name
Type
Description
46778.pdf
pdf
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