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Deanship of Graduate Studies
Document Details
Document Type
:
Thesis
Document Title
:
Feature Extraction Techniques for Computer Aided Diagnosis
تقنيات استخلاص الميزات للتشخيص بمساعدة الحاسوب
Subject
:
Faculty of Engineering
Document Language
:
Arabic
Abstract
:
Breast cancer is one from 100 types of cancer and the most commonly diagnosed in women in Saudi Arabia. In 2015, the mortality rate of this disease was 15.4% of cancer deaths overall. To detect and diagnose breast cancer, Mammography is a noninvasive technique that has been successful in improving detection of cancer particularly non-palpable breast masses and calcifications that may be malignant. So, it continues to be the standard screening tool for breast cancer detection resulting in at least a 30% reduction in breast cancer deaths. Occasionally, Radiologists fail to detect suspicious abnormalities that is repetitive and fatiguing task. Thus, there is a necessity for developing methods for automatic detection and classification of suspicious areas in mammograms with more accuracy, as a means of helping radiologists to improve the efficacy of screening programs and avert unnecessary biopsies. By incorporating the expert knowledge of radiologists, the computer-based systems provide a second opinion in detecting abnormalities and making diagnostic decisions. Such a diagnostic procedure is called computer-aided diagnosis (CAD). A computerized system for such a purpose is called a CAD system. It has been shown that the performance of radiologists can be enhanced by providing them with the results of a CAD system. Hence, there are strong motivations to develop a CAD system to aid radiologists in reading mammograms. This thesis aimed to develop a Computer-Aided Diagnosis (CAD) system by applying eight feature extraction techniques that affected directly on the CAD system attitude. The results that we obtained with MIAS dataset showed that 100% of samples were correctly classified.
Supervisor
:
Prof. Dr. Yasser Mostafa Kadah
Thesis Type
:
Master Thesis
Publishing Year
:
1443 AH
2022 AD
Co-Supervisor
:
Prof. Dr. Ubaid Muhsen Al-Saggaf
Added Date
:
Sunday, March 20, 2022
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
جعفر أحمد العيدروس
AL-aidaros, Gaafar Ahmed
Researcher
Master
Files
File Name
Type
Description
47450.pdf
pdf
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