Automated Lung Tuberculosis Detection Using Chest Radiograph Images CNN–RNN Approach
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Date
2022-03
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Addis Ababa University
Abstract
In the prevailing era, automated identification of diseases becomes a vital for medical technology due to a rapid increase of human population in different parts of the globe. A framework of automated diseases detection based image processing is important to assist radiologists and doctors in the diagnosis/screening of disease and provides more accurate, enhanced diagnosis time, and decrease the mortality rate. Lung tuberculosis has been a severe threat in the current time and it is spreading globally. In order to ameliorate this serious problem, employing an automated detection, identification and diagnosis system will be helpful to enhance the diagnosis speed of this disease and imped it from being spread globally. Many Lung tuberculosis patients in Low and Middle-Income countries die each year due to mistakenly interpret in diagnosis. Developing Accurate Computer-Aided Diagnosis system is helpful for doctors and radiologists to interpret Chest radiograph of a lung tuberculosis patients. Chest radiograph is the most widely used technical tool in medical diagnosis for identification of Lung tuberculosis. However, the interpretations of Chest radiograph might vary from one individual to another. Using correct and early diagnosis imaging technique, the survival rate of the patients with lung tuberculosis is significantly raised.
The proposed method has four major components: preprocessing, lung segmentation, feature extraction and classification. In preprocessing, image quality is enhanced using Gaussian filter and adaptive histogram equalization techniques. Gaussian filter is done for noise avoidance and adaptive histogram equalization is done for image contrast. The output gained from this preprocessed image taken as an input and were performed by thresholding, morphological and Active counter model which used to focus on the lung region or regions of the gained results. The output from this lung segmentation integrated with feature extraction and classification by applying Xception and LSTM architecture. Xception deep convolutional neural network model is a very important model in our thesis to extract the feature of the whole input image (data). And finally LSTM outputs the decision that whether image is TB positive or TB negative. The performance of the proposed computer-assisted diagnostic system for lung TB detection achieves accuracy (86%), precision (90.35%), Recall (85.10%), F1-score (87.65%).
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Keywords
Chest Radiograph, ACM, Thresholding and Morphological Operator, LSTM, Xception-Net