Computer aided detection system for micro-calcifications in digital mammograms /
Hayah Mohamed Bedear
Computer aided detection system for micro-calcifications in digital mammograms / نظام الكشف عن التكلسات الصغرى فى تصوير الثدى بالاشعه السينيه الرقميه بمساعده الحاسوب Hayah Mohamed Bedear ; Supervised Amr Sharawy , Mai S. Mabrok - Cairo : Hayah Mohamed Bedear , 2015 - 115 P. : charts , facsimiles ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Syatems and Biomedical Engineering
Breast cancer continues to be a significant public health problem in the world and the second leading cause of female cancer death following long cancer. Micro calcification clusters and masses are the two most important signs for breast cancer and their automated detection is very valuable for early breast cancer diagnosis early detection is the key for improving breast cancer prognosis mammogram breast x-ray is considered the most reliable method in early detection of breast cancer and the single most effective low cost and highly sensitive technique for detecting small lesions. However, the sensitivity of mammography in highly challenged by the presence of the dense breast parenchyma which deteriorates both detection and characterization taske. The five year survival rate can be increased from 60% to 82% by an early diagnosis of breast cancer
Artificial Neural Network (ANN) K-Nearest Neighbor (KNN) Support Vector Machine (SVM)
Computer aided detection system for micro-calcifications in digital mammograms / نظام الكشف عن التكلسات الصغرى فى تصوير الثدى بالاشعه السينيه الرقميه بمساعده الحاسوب Hayah Mohamed Bedear ; Supervised Amr Sharawy , Mai S. Mabrok - Cairo : Hayah Mohamed Bedear , 2015 - 115 P. : charts , facsimiles ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Syatems and Biomedical Engineering
Breast cancer continues to be a significant public health problem in the world and the second leading cause of female cancer death following long cancer. Micro calcification clusters and masses are the two most important signs for breast cancer and their automated detection is very valuable for early breast cancer diagnosis early detection is the key for improving breast cancer prognosis mammogram breast x-ray is considered the most reliable method in early detection of breast cancer and the single most effective low cost and highly sensitive technique for detecting small lesions. However, the sensitivity of mammography in highly challenged by the presence of the dense breast parenchyma which deteriorates both detection and characterization taske. The five year survival rate can be increased from 60% to 82% by an early diagnosis of breast cancer
Artificial Neural Network (ANN) K-Nearest Neighbor (KNN) Support Vector Machine (SVM)