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What is True Negative (TN)

Handbook of Research on Information Security in Biomedical Signal Processing
It is the number of normal samples detected as normal samples by the classifier algorithm.
Published in Chapter:
Algorithms for Detection and Classification of Abnormality in Mammograms: An Overview
Anuradha Chetan Phadke (Maharashtra Institute of Technology, India) and Priti P. Rege (College of Engineering Pune, India)
DOI: 10.4018/978-1-5225-5152-2.ch013
Abstract
Mammography is a popular imaging modality currently in use for routine screening of breast. Radiologists look for some of the significant signs of breast cancer while examining the mammogram visually. These signs are bounded masses, clusters of micro-calcifications, spiculations, and architectural distortions. Developing computer-aided algorithms for the detection and classification of abnormalities in mammograms is an extremely challenging task because of significant variableness in the type, size, shape, texture variation of abnormal region, and variability in the structure of surrounding tissues of the breast. The main objective of this chapter is to introduce dominant features of various signs of abnormalities and to discuss techniques to detect various abnormalities in mammograms. This knowledge will help to develop a system that is useful for the early detection and classification of breast cancer.
Full Text Chapter Download: US $37.50 Add to Cart
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Text Separation From Document Images: A Deep Learning Approach
It is the number of normal samples detected as normal samples by the classifier algorithm.
Full Text Chapter Download: US $37.50 Add to Cart
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