An efficient classification of Abnormalities in mammogram images Using extreme learning machine

Abstract

Breast cancer is the most invasive cancer, commonly occurring in newlinewomen. It is one of the major reasons for the cause of death among women. newlineEarly detection of breast cancer can be used to the long survival of patients. newlineThe tumour is detected in the breast using a mammography X-ray image. This newlinemammography is one of the special scans that are utilized to discover breast newlinecancer to find the malignant tumour cells in the breast among women at an newlineearly stage to avoid the deaths of the patient. However, assisting radiologists newlineto provide accurate results, and detection of malignant tumours in the breast is newlineone of the challenging issues due to the tumour cells structure. To overcome newlinethese issues, this research proposes the following methods: newlineand#61623; An efficient mammogram image segmentation using visual newlinesaliency mapping with improved Gaussian filtering method newlineand#61623; An improved mammogram image classification using Bat newlineOptimized Runlength Networks (BORN) for breast cancer newlinedetection newlineand#61623; Deep Convolution Method for Detecting Brest Cancer With newlineExtreme Learning Machine newlineIn image segmentation, preprocessing of mammogram images is newlinedone initially for an image quality improvement, in which unnecessary newlinebackground noises are removed from the given mammogram image using an newlineimproved Gaussian filtering method. A conventional Gaussian filtering newlinemethod is enhanced using a number of switching rules in the mammogram newlineimageand#8223;s background noises. newline