Abstract

In this paper, a new computer-aided diagnosis (CAD) system for early lung cancer detection based on the analysis of sputum color images is proposed. A set of features is extracted from the nuclei of the sputum cells after applying a region detection process. For training and testing the system we used two classification techniques: artificial neural network (ANN) and support vector machine (SVM) to increase the accuracy of the CAD system. The performance of the system was analyzed based on different criteria such as sensitivity, precision, specificity and accuracy. The evaluation was done by using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of SVM classifier over the ANN classifier with 97% of sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

Highlights

  • Lung cancer ranks as one of the most common causes of death amongst all diseases

  • In the sputum cell segmentation, we have analyzed the results of the mean shift in gray level feature space and compared them to the results obtained from the Hopfield Neural Network (HNN) proposed in [11]

  • It obtains a high number of true positives (TP) and true negatives (TN), and reduced number of false positives (FP) and false negatives (FN) which leads to successful classification where all the performance criteria measurements are increased and the classification error is highly reduced

Read more

Summary

Introduction

Lung cancer ranks as one of the most common causes of death amongst all diseases. While there have been a lot of approaches to minimize the fatalities caused by this disease, early detection is considered the best step towards effective treatment. The proposed CAD system was tested on 100 sputum color images for early lung cancer detection, the experimental results were substantially improved, with high values of sensitivity, specificity and accuracy, in addition to an accurate detection of the cancerous cells when compared with the pathologist’s diagnosis results. The novelty of this work is defined as follows: a state-of-the-art complete CAD system is implemented based on the sputum color image analysis, and optimal deployment and combination of existing image processing and analysis techniques for building the computer aided diagnosis (CAD) system is used. The contributions can be summarized as follows: (1) Detection of sputum cell using a Bayesian classification framework; (2) Best color space after analysis of the images with histogram analysis; (3) Mean shift technique for the sputum cell segmentation; (4) Feature extraction, where a set of features are extracted from the nucleus region to be used in the diagnosis process.

Background
Sputum Cells Extraction and Segmentation
Cell Detection and Extraction
Segmentation
Experiments
Feature Extraction
Classification
Artificial Neural Network
Support Vector Machine
Comparing the Proposed CAD System with Others
Findings
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call