Investigation of support vector machine for the detection of architectural distortion in mammographic images
نویسندگان
چکیده
This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.
منابع مشابه
Improvement of Breast Cancer Detection Using Non-subsampled Contourlet Transform and Super-Resolution Technique in Mammographic Images
Introduction Breast cancer is one of the most life-threatening conditions among women. Early detection of this disease is the only way to reduce the associated mortality rate. Mammography is a standard method for the early detection of breast cancer. Today, considering the importance of breast cancer detection, computer-aided detection techniques have been employed to increase the quality of ma...
متن کاملA Hybrid Method for Mammography Mass Detection Based on Wavelet Transform
Introduction: Breast cancer is a leading cause of death among females throughout the world. Currently, radiologists are able to detect only 75% of breast cancer cases. Making use of computer-aided design (CAD) can play an important role in helping radiologists perform more accurate diagnoses. Material and Methods: Using our hybrid method, the background and the pectoral muscle...
متن کاملDetection of Glioblastoma Multiforme Tumor in Magnetic Resonance Spectroscopy Based on Support Vector Machine
Introduction: The brain tumor is an abnormal growth of tissue in the brain, which is one of the most important challenges in neurology. Brain tumors have different types. Some brain tumors are benign and some brain tumors are cancerous and malignant. Glioblastoma Multiforme (GBM) is the most common and deadliest malignant brain tumor in adults. The average survival rate for peo...
متن کاملComputer Aided Classification of Architectural Distortion in Mammograms Using Texture Features
Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Architectural distortions, masses and microcalcifications are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four typ...
متن کاملDetection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods
Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...
متن کامل