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Computer aided detection of tuberculosis using two classifiers

  • Abdullahi Umar Ibrahim ORCID logo EMAIL logo , Fadi Al-Turjman , Mehmet Ozsoz and Sertan Serte
Published/Copyright: September 26, 2022
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Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 67 Issue 6

Abstracts

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscopic slide images of tuberculosis into positive and negative cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East university hospital and Kaggle repository. For classification of tuberculosis and healthy microscopic slide using AlexNet+Softmax, the model achieved accuracy of 98.14%. For classification of tuberculosis and healthy microscopic slide using AlexNet+SVM, the model achieved 98.73% accuracy. For classification of tuberculosis and healthy chest X-ray images using AlexNet+Softmax, the model achieved accuracy of 98.19%. For classification of tuberculosis and healthy chest X-ray images using AlexNet+SVM, the model achieved 98.38% accuracy. The result obtained has shown to outperformed several studies in the current literature. Future studies will attempt to integrate Internet of Medical Things (IoMT) for the design of IoMT/AI-enabled platform for detection of Tuberculosis from both X-ray and Microscopic slide images.

Keywords: AlexNet; chest X-ray; deep learning; microscopic slide; SVM; tuberculosis
Corresponding author: Abdullahi Umar Ibrahim, Department of Biomedical Engineering, Near East University, Nicosia, Mersin 10, Turkey, E-mail:

Acknowledgments

We will like to acknowledge the staffs of Pathology department of Near East Hospital for providing with all the assistance.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: The local Institutional Review Board deemed the study exempt from review.

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Received: 2021-09-20
Accepted: 2022-09-13
Published Online: 2022-09-26
Published in Print: 2022-12-16

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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DOI:  doi.org/10.1515/bmt-2021-0310
Keywords for this article
AlexNet; chest X-ray; deep learning; microscopic slide; SVM; tuberculosis

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. Nano-material utilization in stem cells for regenerative medicine
  4. Active cell capturing for organ-on-a-chip systems: a review
  5. Research Articles
  6. Towards technically controlled bioreactor maturation of tissue-engineered heart valves
  7. In vitro thrombogenicity evaluation of rotary blood pumps by thromboelastometry
  8. Effects of weight gaining to lower limb joint moments: a gender-specific sit-to-stand analysis
  9. Evaluation method of ex vivo porcine liver reduced scattering coefficient during microwave ablation based on temperature
  10. Optical bone densitometry insensitive to skin thickness
  11. Computer aided detection of tuberculosis using two classifiers
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