Detection masses in digital mammography images using neural networks Thesis

Detection masses in digital mammography visualises using aflutter networks - Thesis ExampleIn film screen mammography, special films and intensifying screens are used to detect breast cancer. FSM provides high quality delineations at low radiation doses (DeFelice 2002, p. 12). Denise and Farleigh (2005) assert, The major limitation of traditional mammography is that the film serves simultaneously as the image receptor, display medium, and long term storage medium for the image. Digital mammography makes use of solid-state detectors in order to display images of breasts on the computer screen. Denise and Farleigh (2005) put in that separation of image acquisition, image processing, and display to be one of the principal advantages of digital imaging system. Digital mammography also makes use of CAD (Computer-Aided Detection), which assists the physicians in image commentary.Mass contracting in mammograms refers to the detection of those groups of cells that cause breast cancer. Bick and Diekmann (2010, p.100) found that sensitivity to be not high enough in mass detection. Computer-aided detection system, cellular skittish networks, a two-stage hybrid classification network, and some other techniques can be used for mass detection. Bruynooghe (2006), in an article, found that in example of hybrid network, an unsupervised classifier is used to examine suspicious opacities, and then some supervised interpretation rules are applied to reduce false detections. Cellular neural networks play a vital role in mass detection. Kupinski and Giger (2002) showed in a research that features extracted from potential lesion areas are sent through a neural network to decide whether the area is a true lesion or a false detection. Using CAD as a system for image interpretation is very facilitating for the physicians. However, some researchers suggest improvements in the current CAD technology. One of those suggestions includes development of a CAD system with increased abi lity to detect echt abnormalities instead of marking