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Automatic Lung Tuberculosis Detection Model Using Thorax Radiography Image
Abstract
Tuberculosis (TB) is a communal disease with high death and disease rates worldwide. The chest radiograph (CXR) is commonly used in diagnostic solutions for lung TB. Automatic computer-aided solutions to identify TB using CXRs and can advance the efficiency of the diagnostic of TB. In this chapter, an automatic TB detection model using CXR image is proposed. By identifying open issues include how detect the lung region automatically and what are the features, one can identify if a given CXR image is infected or normal using three public datasets such as Schengen, Montgomery Country (MC), and JSRT. The possible textural features of a lung object are obtained from the first-order and second-order gray level co-occurrence matrix (GLCM) statistical features. The performance of the proposed model was evaluated using accuracy, sensitivity, and specificity, and the model achieved AUC 91%, 62%, 71%, and 81% on Schengen, JSRT, MC, and combined datasets.
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