Automatic detection of tuberculosis based on AdaBoost classifier and genetic algorithm

Tuberculosis is one of the most commonly affected diseases in the progressing countries. Early stage diagnosis of tuberculosis plays a significant role in curing TB patients. The work presented in this paper is focused on design and development of a system for the detection of tuberculosis in CT lung images. The disease can be diagnosed easily by radiologists with the help of automated tuberculosis detection system. The main objective of this paper is to get best solution selected by means of genetic programming is regarded as optimal feature descriptor. Five stages are being used to detect tuberculosis disease. They are pre-processing the image, segmentation, extracting the feature, feature selection and classification. These stages are used in medical image processing to enhance the TB identification. In feature extraction stage, wavelet-based statistical texture feature extraction is used to extract the features and from the extracted feature sets the optimal features are selected by genetic algorithm. Finally, AdaBoost classifier method is used for image classification. The experimentation is done and intermediate results are obtained. The experimental results show that AdaBoost is a good classifier, giving an accuracy of 93% for classifying TB affected and non-affected lungs using wavelet-based statistical texture features.