Bird Mating Optimizer with Deep Learning-based Tuberculosis Detection using Chest Radiographs

Bird Mating Optimizer with Deep Learning-based Tuberculosis Detection using Chest Radiographs

© 2023 by IJETT Journal
Volume-71 Issue-2
Year of Publication : 2023
Author : K. Manivannan, S. Sathiamoorthy
DOI : 10.14445/22315381/IJETT-V71I2P236

How to Cite?

K. Manivannan, S. Sathiamoorthy, "Bird Mating Optimizer with Deep Learning-based Tuberculosis Detection using Chest Radiographs," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 341-348, 2023. Crossref,

Tuberculosis (TB) is a prolonged lung illness that affects by pneumonia cases and results in a high mortality rate. TB detection is a tedious process that is primarily due to different kinds of manifestations, namely focal lesions, large opacities, aggregation, cavities, CXR image nodules, and small opacities. Early and accurate detection of TB is of considerable importance, or else it could be life-threatening. Machine learning (ML) is a subdivision of computing that analyses algorithms with the capability to “learn.” The study of health interest images with deep learning (DL) could not be constrained to the usage of medical diagnosis. In this study, we propose a bird mating optimizer with deep learning-based TB detection and classification (BMODL-TBDC) system on chest radiographs. The presented BMODL-TBDC technique applies median filtering (MF) for noise removal. For feature extraction, the Xception architecture is used with the BMO algorithm as a hyperparameter optimizer. Finally, boosted convolutional autoencoder (BCAE) is applied for TB detection purposes. The simulation outcome of the BMODL-TBDC approach on the benchmark medical database reports promising performance over other recent systems with respect to various measures.

Tuberculosis, Chest radiographs, Machine learning, Deep learning, Bird mating optimizer.

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