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A Novel Approach to Detect Microcalcification for Accurate Detection for Diagnosis of Breast Cancer

  • P. P. PatilEmail author
  • S. Kotrappa
Chapter
  • 19 Downloads
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 266)

Abstract

Breast Cancer is one in all the top cancers inflicting death of assorted woman’s across the globe. It is really dangerous but at the same can be cured completely in case of early detection of it happens. Worldwide radiologist and doctors are working on various techniques and they found that mammographic identification of breast cancer images can be important in early detection of breast tissue cancer. Primary and main indication of the breast cancer is presenting the clusters of microcalcification. Although it becomes very difficult and also time consuming to make the arrangement of microcalcifications by the radiologists as benign or malignant. Also, the interpretation of mammograms isn’t straightforward thanks to tiny variations in densities of various tissues at intervals the image. It’s absolutely matching case for dense breast. This work presents novel approach that can perform classification of the clusters which is part of micro classification presents in the mammograms. In case of multiscale morphology, it observed the inter-connectivity of each microcalcification. This work is different one from the existing which mainly has attainment on the morphology of the microcalcification and the cluster feature for representing the cluster structure of microcalcification it generates a graphs. Theoretical features of a graph are extracted to maintain topological features used to make classification of the clusters of microcalcification using the k-nearest neighbour algorithm. This projected work evaluated with the assistance of 2 most accepted digitized dataset referred to as MIAS and DDSM and capable digital dataset. This work shows the different topology modeling is extremely necessary for accurate estimation of the microcalcification. Microcalcification thanks to the improved accuracy of microcalcification and conjointly the topological measures that is in a position to couple with clinical understanding.

Keywords

Classification Graphs Rough set Mammography Microcalcification Topology 

Notes

Acknowledgements

I would like to express my deep thanks to Dr. S. Kotrappa sir for timely valuable guidance and motivation. The way we carried out the entire survey and started working together your valuable inputs leads me too learn from the failures and experiments. The way you made resources available was helped me a lot. Thank you for your cooperation and contribution.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and EngineeringVTUBelgaumIndia
  2. 2.Computer EngineeringVTU, Dr. MSS Sheshgiri College of Engineering and TechnologyBelgaumIndia

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