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Original scientific paper

Application of Artificial Neural Networks for Direct Detection of Microcalcification Clusters in Digital Mammograms

Thierry-Pascal Baum ; Orly, France

Fulltext: english, pdf (5 MB) pages 425-433 downloads: 78* cite
APA 6th Edition
Baum, T. (1998). Application of Artificial Neural Networks for Direct Detection of Microcalcification Clusters in Digital Mammograms. Journal of computing and information technology, 6 (4), 425-433. Retrieved from https://hrcak.srce.hr/150208
MLA 8th Edition
Baum, Thierry-Pascal. "Application of Artificial Neural Networks for Direct Detection of Microcalcification Clusters in Digital Mammograms." Journal of computing and information technology, vol. 6, no. 4, 1998, pp. 425-433. https://hrcak.srce.hr/150208. Accessed 23 Apr. 2021.
Chicago 17th Edition
Baum, Thierry-Pascal. "Application of Artificial Neural Networks for Direct Detection of Microcalcification Clusters in Digital Mammograms." Journal of computing and information technology 6, no. 4 (1998): 425-433. https://hrcak.srce.hr/150208
Harvard
Baum, T. (1998). 'Application of Artificial Neural Networks for Direct Detection of Microcalcification Clusters in Digital Mammograms', Journal of computing and information technology, 6(4), pp. 425-433. Available at: https://hrcak.srce.hr/150208 (Accessed 23 April 2021)
Vancouver
Baum T. Application of Artificial Neural Networks for Direct Detection of Microcalcification Clusters in Digital Mammograms. Journal of computing and information technology [Internet]. 1998 [cited 2021 April 23];6(4):425-433. Available from: https://hrcak.srce.hr/150208
IEEE
T. Baum, "Application of Artificial Neural Networks for Direct Detection of Microcalcification Clusters in Digital Mammograms", Journal of computing and information technology, vol.6, no. 4, pp. 425-433, 1998. [Online]. Available: https://hrcak.srce.hr/150208. [Accessed: 23 April 2021]

Abstracts
Computer-aided diagnosis (CAD) schemes for the detection of microcalcification clusters (MCCs) come in two types: indirect and direct. Indirect detection of MCCs detect individual microcalcifications first, which are then used to detect clusters. Direct detection detects clusters in a unique step, without any previous detection of individual microcalcifications. Nearly all the existing literature describes indirect detection. In this study, we investigated a direct detection scheme. We divided digital mammograms into regions of interest (ROis) and computed a set of parameters on each ROI. We discriminated parameters through an artificial neural network (ANN) that gave the presence or absence of an MCC in the examined ROI. Final images with suspicious ROis containing MCCs were shown to radiologists. Results appeared to be interesting enough to compete with indirect detections. Extra studies could prove direct detection to be a better approach as compared to indirect detection CAD schemes.

Keywords
breast cancer; digital mammography; mammograms; artificial neural networks; direct detection; indirect detection; clusters of microcalcifications; computer-aided diagnosis

Hrčak ID: 150208

URI
https://hrcak.srce.hr/150208

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