Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization

Tawsifur Rahman,Khandaker Rejaul Islam,Mohamed Arselene Ayari,Muhammad E H Chowdhury,Saad Kashem,Amith Khandakar,Khandakar F Islam,Muhammad Abdul Kadir,Zaid Bin Mahbub,Tahir Hamid,Mohammad Tariqul Islam,Rashid Mazhar

doi:10.1109/access.2020.3031384

Abstract

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 700 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet), which were used for transfer learning from their pre-trained initial weights and trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images, and segmented lung images. The accuracy, precision, sensitivity, F1-score, specificity in the detection of tuberculosis using X-ray images were 97.07 %, 97.34 %, 97.07 %, 97.14 % and 97.36 % respectively. However, segmented lungs for the classification outperformed than whole X-ray image-based classification and accuracy, precision, sensitivity, F1-score, specificity were 99.9 %, 99.91 %, 99.9 %, 99.9 %, and 99.52 % respectively. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions results in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.

Highlights

Tuberculosis (TB) is a communicable disease caused by a bacterium called Mycobacterium tuberculosis
This paper focuses on the detection of TB using transfer learning based technique of convolutional neural networks (CNNs) on the original and segmented lungs in X-ray images
It can be noted that the original U-Net outperformed modified U-Net in the segmentation of lung regions on Chest X-rays (CXR) images quantitatively and qualitatively

Summary

Introduction

Tuberculosis (TB) is a communicable disease caused by a bacterium called Mycobacterium tuberculosis. Leading cause of death from a single infectious disease [1]. A. DEEP CONVOLUTIONAL NEURAL NETWORKS (CNNS) BASED TRANSFER LEARNING. Transfer learning can be useful in those applications of CNN where the dataset is not large. Transfer learning has been successfully used in various field applications such as manufacturing, medical and baggage screening [44]–[46]. This removes the requirement of having large dataset and reduces the long training period as is required by the deep learning algorithm when developed from scratch [47], [48]

Methods

Results

Conclusion