تصنيف اورام الدماغ باستخدام الشبكات العصبية الاصطناعية
Author name:
نور حيدر داخل
Supervisor name:
حازم باقر طاهر العلي
General topic:
Computer Science
Specific topic:
Computer Science
Degree:
Master
University:
University of Thi-Qar - College Of Education For Pure Sciences - Department Of Computer
Language:
English
University location:
Dhi Qar
First pages:
28T763 - p.pdf
Abstract:
Accelerating the pace of scientific progress and that focus in the field of human service requirements is to provide daily health and service. Health aspect is one of the most important things of science that needs to be develop it because it has an important effect on human life. Medical imaging has become one of the most important factors in the treatment of human health. A large amount of information and data can be obtained about disease by different methods of Images acquisition techniques same like Magnetic Resonance Imaging (MRI), Computed Tomography (CT).The purpose of this thesis is to discover a way to detect the brain is to suggest a system to diagnose the brain whether it is normal or abnormal (contains a tumor) by using artificial neural network technology which contain many steps. The first step of detecting the brain tumor system is the input image to the system as digital data.The processing is the second step, which includes reducing noise and removing impurities of the brain image. So as the noise is removed by using median filter and wiener filter. After processing (filtration) image segmentation. Feature extraction is the techniques that is used to measure of difference properties of image segments, given the specific features as input to the artificial neural network classified (ANN).Seeded classified databases are due to the two types of brain image, (normal or abnormal).Samples have taken from a group of patients in AL - Husain Teaching Hospital disparate ages and both ganders, which was 100 samples ,80 of them were training samples and 20 were tested .The proposed system has proven successful of the diagnostic through the results obtained on the ratio of (100% in the training phase) and (100% in the testing phase
