A hybrid architecture of DenseNet201 and XGBoost to detect tuberculosis from chest x-ray

Muhammad Rahman; Yongzhong Cao; Bin Li; Xiaobing Sun; Yameng Hao

doi:10.1117/12.2606841

28 October 2021 A hybrid architecture of DenseNet201 and XGBoost to detect tuberculosis from chest x-ray

Muhammad Rahman, Yongzhong Cao, Bin Li, Xiaobing Sun, Yameng Hao

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841Q (2021) https://doi.org/10.1117/12.2606841
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Deep neural networks are frequently used to automate the examination of radiographic images in medical. These approaches may be used to train on huge datasets or extract features from small datasets using pre-trained networks. Due to the lack of large pulmonary tuberculosis datasets, it is possible to diagnose tuberculosis using pre-trained deep convolutional neural networks. Thus, this article aims to detect and diagnose tuberculosis in chest X-rays by combining a pre-trained deep convolutional neural network with a machine learning model. Combined the deep pre-trained DenseNet201 network with the machine learning XGBoost classifier to create a hybrid model for classifying patients as tuberculosis infected or not. The proposed model extracts feature using the pre-trained DenseNet201 neural networks and classify them employing the XGBoost classifier. We performed extensive experiments to assess the performance of the proposed DenseNet201-XGBoost model using tuberculosis chest x-ray images. Comparative study shows that the proposed DenseNet201-XGBoost-based tuberculosis classification model outperforms other competing approaches.

Citation Download Citation

Muhammad Rahman, Yongzhong Cao, Bin Li, Xiaobing Sun, and Yameng Hao "A hybrid architecture of DenseNet201 and XGBoost to detect tuberculosis from chest x-ray", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841Q (28 October 2021); https://doi.org/10.1117/12.2606841

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Feature extraction

Chest imaging

Convolution

Data modeling

Diagnostics

Machine learning

Performance modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years