Automatic Tuberculosis Detection Using Chest X-ray Analysis With Position Enhanced Structural Information
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
18-5-2020
Abstract
Tuberculosis is a disease responsible for the deaths of more than one million people worldwide every year. Even though the disease is preventable and curable, it remains a major threat to the humanity that needs to be taken care of. More developed countries use advanced techniques such as culture methods or sputum smear microscopy to diagnose the disease. However, since those approaches are rather expensive, they are not commonly used in poor regions of the globe such as East Asia, Africa and Bangladesh. Instead the well know and very affordable chest x-ray interpretation by radiologists is the technique employed in those places. Some of the major issues with this approach are: i) is a tedious task that requires experienced medical personnel, and ii) is performed manually which can be very painful when done for a large population. In order to accelerate the interpretation process and reduce the dependence on qualified radiologists -which is scarce it those countries, many software solutions evolved over the last few years considering computer vision, artificial intelligence and machine learning. The issue with these solutions is that they are either not reliable enough or they are rather complicated. Therefore, we propose a fully automatic software solution that uses only machine learning and image processing to analyse and detect anomalies related to Tuberculosis in Chest x-rays images. Our system has been tested on two benchmark data collections -Montgomery and Shenzhen, and produced state-of-the-art results reaching up to 97% in accuracy. College of the Sciences Presentation Award Winner.
Recommended Citation
Nkouanga, Hermann Yepdijo, "Automatic Tuberculosis Detection Using Chest X-ray Analysis With Position Enhanced Structural Information" (2020). Symposium Of University Research and Creative Expression (SOURCE). 53.
https://digitalcommons.cwu.edu/source/2020/COTS/53
Department/Program
Computer Sciences
Additional Mentoring Department
https://cwu.studentopportunitycenter.com/2020/04/automatic-tuberculosis-detection-using-chest-x-ray-analysis-with-position-enhanced-structural-information/
Automatic Tuberculosis Detection Using Chest X-ray Analysis With Position Enhanced Structural Information
Ellensburg
Tuberculosis is a disease responsible for the deaths of more than one million people worldwide every year. Even though the disease is preventable and curable, it remains a major threat to the humanity that needs to be taken care of. More developed countries use advanced techniques such as culture methods or sputum smear microscopy to diagnose the disease. However, since those approaches are rather expensive, they are not commonly used in poor regions of the globe such as East Asia, Africa and Bangladesh. Instead the well know and very affordable chest x-ray interpretation by radiologists is the technique employed in those places. Some of the major issues with this approach are: i) is a tedious task that requires experienced medical personnel, and ii) is performed manually which can be very painful when done for a large population. In order to accelerate the interpretation process and reduce the dependence on qualified radiologists -which is scarce it those countries, many software solutions evolved over the last few years considering computer vision, artificial intelligence and machine learning. The issue with these solutions is that they are either not reliable enough or they are rather complicated. Therefore, we propose a fully automatic software solution that uses only machine learning and image processing to analyse and detect anomalies related to Tuberculosis in Chest x-rays images. Our system has been tested on two benchmark data collections -Montgomery and Shenzhen, and produced state-of-the-art results reaching up to 97% in accuracy. College of the Sciences Presentation Award Winner.
https://digitalcommons.cwu.edu/source/2020/COTS/53
Faculty Mentor(s)
Szilard Vajda