Computer aided diagnosis system for early lung cancer detection

Fatma Taher, Naoufel Werghi, Hussain Al-Ahmad

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD) system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromaticfeatures are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM) for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity,specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

Original languageBritish English
Pages (from-to)1088-1110
Number of pages23
Issue number4
StatePublished - 2015


  • Bayesian theorem
  • Compute-aided diagnosis
  • Feature extraction
  • Lung cancer detection
  • Mean shift segmentation
  • Neural network
  • Sputum images
  • Support vector machine


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