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A Deep Multi-Modal Approach for Automatic Analysis of Tuberculosis-related Lung Diseases Using 2D CNN and Bi-LSTM

Alebiosu David Olayemi, Anuja Dharmaratne, Lim Chern Hong

Monash University, Malaysia

Tuberculosis is a deadly disease and it is caused by a germ called mycobacterium. Tuberculosis is a bacterial infection which usually affects the lungs but can be treated if early diagnosis is carried out on the patient. Though it was discovered about 130 years ago, it has continued to remain a constant threat and has become a major cause of death across the globe. Presently, tuberculosis is the leading infectious diseases which have caused millions of deaths, giving rise to an estimated 2.5 million new cases every year. The need to pay urgent attention to effective diagnosis of tuberculosis is becoming increasingly clear with the recent outbreak of the novel COVID-19 pandemic across the globe. Tuberculosis has been found to be one of the most serious underlying health conditions that can slow down the recovery process of any COVID-19 patient. In medical imaging, the use of handcrafted techniques previously employed by researchers for CT image analysis has been ineffective due to their limitations in extracting discriminative features from the images. Over the last 10 years, deep learning applications have been employed to eradicate the challenges faced by the handcrafted techniques and has produced outstanding results. This research aims to employ the use of two deep learning architectures for the automatic analysis of chest CT scan images for tuberculosis severity assessment. ImageCLEF 2019 CT images used for this work were first resized before serving as input into a 2dimensional (2D) Convolutional Neural Network(CNN) and Bi-directional Long Short Time Memory (Bi-LSTM) for the classification task. The proposed technique produced an overall Area Under Curve (AUC) and Classification Accuracy (ACC) of 0.87 and 0.89 respectively. This is an improvement compared to some of the runs submitted for the ImageCLEF 2019 tuberculosis task.