Advanced Applications for Artificial Neural Networks 2018
DOI: 10.5772/intechopen.71256
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Breast Cancer Detection by Means of Artificial Neural Networks

Abstract: Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this researc… Show more

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Cited by 6 publications
(5 citation statements)
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“…Even when lesions are successfully detected, diagnostic delays can occur due to assessment or management recommendation errors [23]. Therefore, Artificial Intelligent technology is being designed to eliminate the unnecessary waiting time as well as reducing human and technical errors in diagnosing BC [24]. Gandhi et al [25] found that 59% of diagnostic errors were caused by three or more process breakdowns, delaying diagnosis by an average of more than a year.…”
Section: Discussionmentioning
confidence: 99%
“…Even when lesions are successfully detected, diagnostic delays can occur due to assessment or management recommendation errors [23]. Therefore, Artificial Intelligent technology is being designed to eliminate the unnecessary waiting time as well as reducing human and technical errors in diagnosing BC [24]. Gandhi et al [25] found that 59% of diagnostic errors were caused by three or more process breakdowns, delaying diagnosis by an average of more than a year.…”
Section: Discussionmentioning
confidence: 99%
“…For the extraction and registration of the insects, the regions of interest (ROIs) property is applied, which allows such features as eccentricity, area, centroid, solidity, and others to be obtained [42,43].…”
Section: Regions Of Interestmentioning
confidence: 99%
“…As a result, there are always background elements, especially those associated with the pectoral muscle region. Both of these regions may contain prenoise, such as the labels used by radiologists to identify certain features and patient information [12], noise (such as salt and pepper noise, gaussian noise, speckling and Poisson noise) and artifacts that can influence the performance of a CAD system [13]. The areas of poor contrast, high and low intensity and the mix of regular or irregular shapes in the pectoral muscle region and around the labels can also confuse CAD systems [14].…”
Section: Introductionmentioning
confidence: 99%