Lung Cancer Detection with FPCM and Watershed Segmentation Algorithms

  • N. Bhaskar
  • T. S. GanashreeEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


Lung malignant growth drives the causes among disease related passing around the world. WHO information showed 1.69 million passing away in 2015. An early disease investigation can enhance the viability of therapy and also upgrades victim’s possibility of exist. The Precision of disease investigation, Rate and Computerization levels decides the achievement of CAD frameworks. In this paper, we worked with the few effectively existing frameworks and discovered the best methodology for recognition of tumors. This article talks about the division with FPCM and Watershed Transform calculations. Computer-aided design includes six stages – a. Image Acquisition, b. Image Pre-processing, c. Lung Region Extraction, d. Segmentation, e. Feature Extraction and f. Classification. Firstly, RGB picture is transfers to dark scale thus the picture clamour is a greater distanced from original picture. Next essential job is division which is performed by utilizing Watershed Transform display. Watershed method characterizes the dark scale picture. After division, highlight extraction is dissected by mean of the fragmented lung region lastly delineation of lung lumps classifies with the help of SVM method. By using this method we accomplished precision: 99% and the time are less than 2 s. The proposed frameworks were executed in MATLAB programming.


FPCM Watershed Transform Segmentation SVM (Support Vector Machine) 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.VTU-RRC, Visvesvaraya Technological UniversityBelagaviIndia
  2. 2.CSE DepartmentCMR Technical CampusKandlakoyaIndia
  3. 3.Department of Telecommunication EngineeringDayanandasagar College of EngineeringBangaloreIndia

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