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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Stefanus Kieu Tao Hwa
Universiti Malaysia Sabah
Computer Science & IT Engineering

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Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images Stefanus Kieu Tao Hwa; Mohd Hanafi Ahmad Hijazi; Abdullah Bade; Razali Yaakob; Mohammad Saffree Jeffree
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (333.062 KB) | DOI: 10.11591/ijai.v8.i4.pp429-435

Abstract

Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.
Co-Authors Abdullah Bade Mohammad Saffree Jeffree Mohd Hanafi Ahmad Hijazi Razali Yaakob