Early Detection of Tuberculosis using Chest X-Ray (CXR) with Computer-Aided Diagnosis

In this paper, a Computer-aided Diagnosis (CADx) system based on image processing is proposed to assist doctors and radiologists in interpreting Chest X-rays (CXR) for early detection of lung Tuberculosis (TB). CXR can indicate lung abnormalities including TB. However, the interpretations of CXR might vary from one individual to another. It is important to accurately and quickly detect TB because early treatment will prevent more infections and fatal effects from happening. The steps that were performed by the proposed system consisted of preprocessing, segmentation, feature extraction, and classification. In the preprocessing stage homomorphic filter, histogram equalization, median filter, and Contrast-Limited Adaptive Histogram Equalization (CLAHE) were applied to increase image quality. Segmentation was done by using Active Contour Model. Feature extraction was performed by analyzing the image's first order statistical features. The last stage, classification, was based on the mean values. The results indicated that the system can increase specificity while maintaining sensitivity and accuracy of TB diagnosis. In conclusion, there is a high chance that CADx can assist doctors and radiologists for a more accurate and quick interpretation of CXR in early detection of TB.