Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images

Stefanus Kieu Tao Hwa; Mohd Hanafi Ahmad Hijazi; Abdullah BadeRazali YaakobMohammad Saffree Jeffree

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Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images

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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.

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来源
《IAES International Journal of Artificial Intelligence》 |2019年第4期|429-435|共7页
作者
Stefanus Kieu Tao Hwa; Mohd Hanafi Ahmad Hijazi; Abdullah BadeRazali YaakobMohammad Saffree Jeffree;
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作者单位

Faculty of Science and Natural Resources, Universiti Malaysia Sabah;

Faculty of Computing and Informatics, Universiti Malaysia Sabah;

Faculty of Computer Science and Information Technology, Universiti Putra MalaysiaFaculty of Medicine and Health Sciences, Universiti Malaysia Sabah;

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原文格式 PDF
正文语种英语
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关键词
Canny edge detector; Deep learning; Ensemble; Medical image analysis; Tuberculosis detection;

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Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images

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