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 TypeDr GradeEmail
جعفر أحمد العيدروسAL-aidaros, Gaafar AhmedResearcherMaster 

Files

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 47450.pdf pdf 

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