Artificial Neural Network and Fuzzy Clustering Methods in Segmenting Sputum Color Images for Lung Cancer Diagnosis

  • Fatma Taher
  • Rachid Sammouda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

Lung cancer is cancer that starts in the lungs. Cancer is a disease where cancerous cells grow out of control, taking over normal cells and organs in the body. The early detection of lung cancer is the most effective way to decrease the mortality rate. In this paper we compare two methods, a modified Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) Clustering Algorithm, used in segmenting sputum color images. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer. Both methods are designed to classify the image of N pixels among M classes or regions. Due to intensity variations in the background of the raw images, a pre-segmentation process is developed to standardize the segmentation process. In this study, we used 1000 sputum color images to test both methods, and HNN has shown a better classification result than FCM; however the latter was faster in converging.

Keywords

Lung cancer Diagnosis Sputum Cells Hopfield Neural Network Fuzzy C-Mean Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fatma Taher
    • 1
  • Rachid Sammouda
    • 2
  1. 1.Department of Computer Engineeringkhalifa UniversitySharjahUnited Arab Emirates
  2. 2.Department of Computer scienceUniversity of SharjahSharjahUnited Arab Emirates

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