A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection


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TAŞCI A. E. , Uluturk C., UĞUR A.

NEURAL COMPUTING & APPLICATIONS, vol.33, no.22, pp.15541-15555, 2021 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 22
  • Publication Date: 2021
  • Doi Number: 10.1007/s00521-021-06177-2
  • Title of Journal : NEURAL COMPUTING & APPLICATIONS
  • Page Numbers: pp.15541-15555
  • Keywords: Tuberculosis detection, Pattern recognition, Deep learning, Fine-tuning, Image processing, Voting, Ensemble learning, Augmentation, CLAHE, ADAPTIVE HISTOGRAM EQUALIZATION, CHEST

Abstract

Tuberculosis (TB) is known as a potentially dangerous and infectious disease that affects mostly lungs worldwide. The detection and treatment of TB at an early stage are critical for preventing the disease and decreasing the risk of mortality and transmission of it to others. Nowadays, as the most common medical imaging technique, chest radiography (CXR) is useful for determining thoracic diseases. Computer-aided detection (CADe) systems are also crucial mechanisms to provide more reliable, efficient, and systematic approaches with accelerating the decision-making process of clinicians. In this study, we propose voting and preprocessing variations-based ensemble CNN model for TB detection. We utilize 40 different variations in fine-tuned CNN models based on InceptionV3 and Xception by also using CLAHE (contrast-limited adaptive histogram equalization) preprocessing technique and 10 different image transformations for data augmentation types. After analyzing all these combination schemes, three or five best classifier models are selected as base learners for voting operations. We apply the Bayesian optimization-based weighted voting and the average of probabilities as a combination rule in soft voting methods on two TB CXR image datasets to get better results in various numbers of models. The computational results indicate that the proposed method achieves 97.500% and 97.699% accuracy rates on Montgomery and Shenzhen datasets, respectively. Furthermore, our method outperforms state-of-the-art results for the two TB detection datasets in terms of accuracy rate.