Detection of Pulmonary Tuberculosis Manifestation in Chest X-Rays Using Different Convolutional Neural Network (CNN) Models
Syeda Shaizadi Meraj1, Razali Yaakob2, Azreen Azman3, Siti Nurulain Mohd Rum4, Azree Shahrel Ahmad Nazri5, Nor Fadhlina Zakaria6

1Syeda Shaizadi Meraj, Faculty of Computer Science and Information Technology.
2Razali Yaakob, Faculty of Computer Science and Information Technology.
3Azreen Azman, Faculty of Computer Science and Information Technology.
4Siti Nurulain Mohd Rum, Faculty of Computer Science and Information Technology.
5AzreeShahrel Ahmad Nazri, Faculty of Computer Science and Information Technology.
6Nor Fadhlina Zakaria, Department of Medicine, Medical and Health Science Faculty, Universiti Putra Malaysia, 43400, Serdang, Selangor.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2270-2275 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2632109119/2019©BEIESP | DOI: 10.35940/ijeat.A2632.109119
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Abstract: Tuberculosis (TB) is airborne infectious disease which has claimed many lives than any other infectious disease. Chest X-rays (CXRs) are often used in recognizing TB manifestation site in chest. Lately, CXRs are taken in digital formats, which has made a huge impact in rapid diagnosis using automated systems in medical field. In our current work, four simple Convolutional Neural Networks (CNN) models such as VGG-16, VGG-19, RestNet50, and GoogLenet are implemented in identification of TB manifested CXRs. Two public TB image datasets were utilized to conduct this research. This study was carried out to explore the limit of accuracies and AUCs acquired by simple and small-scale CNN with complex and large-scale CNN models. The results achieved from this work are compared with results of two previous studies. The results indicate that our proposed VGG-16 model has gained highest score overall compared to the models from other two previous studies.
Keywords: Tuberculosis, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNN), Deep Learning (DL)