Lung cancer detection using labeled sputum sample: multi spectrum approach

  • Authors:

  • K. Kancherla;S. Mukkamala

  • Affiliations:

  • Institute for Complex Additive Systems and Analysis, Computational Analysis and Network Enterprise Solutuons, New Mexico Institute of Mining and Technology, Socorro, New Mexico;Institute for Complex Additive Systems and Analysis, Computational Analysis and Network Enterprise Solutuons, New Mexico Institute of Mining and Technology, Socorro, New Mexico

  • Venue:
  • IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
  • Year:
  • 2011

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Abstract

In this paper we demonstrate the use of multi-spectrum imaging and machine learning techniques for automated detection of lung cancer. The sputum samples from patients are first stained using Tetrakis Carboxy Phenyl Porphine (TCPP). Tetrakis Carboxy Phenyl Porphine (TCPP) is a porphyrin molecular marker which binds to cancer and pre cancerous cells, causing cancer cells to glow red under fluorescent microscope. After the sputum samples are stained, images are taken at multiple frequencies 650nm and 660nm. We extracted four different sets of features (shape based, intensity based, wavelet based and Gabor filter based features). Both wavelet based and Gabor based features capture the texture properties of cell. Using these features we built different machine learning models. We obtained an accuracy of 96% using initial set of 35 features (shape based, intensity based and wavelet based features). After adding Gabor based features to this initial set, we obtained accuracy of about 98%. Our experiments show the potential of using TCPP stain, machine learning techniques and Multi-spectrum imaging for early detection of lung cancer.