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

Bird Mating Optimizer with Deep Learning-based Tuberculosis Detection using Chest Radiographs
  IJETT-book-cover           
  
© 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, https://doi.org/10.14445/22315381/IJETT-V71I2P236

Abstract
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.

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

References
[1] Mostofa Ahsan, Rahul Gomes, and Anne Denton, “Application of a Convolutional Neural Network Using Transfer Learning for Tuberculosis Detection,” 2019 IEEE International Conference on Electro Information Technology (EIT), IEEE, pp. 427-433, 2019. Crossref, http://doi.org/10.1109/EIT.2019.8833768
[2] Omar Faruk et al., “A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning,” Journal of Healthcare Engineering, 2021. Crossref, https://doi.org/10.1155/2021/1002799
[3] Linh T. Duong et al., “Detection of Tuberculosis from Chest X-Ray Images: Boosting the Performance with Vision Transformer and Transfer Learning,” Expert Systems with Applications, vol. 184, p.115519, 2021.
[4] Shuangquan Li et al., “Health Checkup could Reveal Chronic Disorders with Support from Artificial Intelligence,” International Journal of Engineering Trends and Technology, vol. 67, no. 11, pp. 8-15, 2019. Crossref, https://doi.org/10.14445/22315381/IJETTV67I11P202
[5] Dilmurod Nabiev, and Khayit Turaev, "Study of Synthesis and Pigment Characteristics of the Composition of Copper Phthalocyanine with Terephthalic Acid," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 1-9, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P201
[6] Shwetambari Borade et al., "Deep Scattering Convolutional Network for Cosmetic Skin Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 10-23, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P202
[7] Khairul Munadi et al., “Image Enhancement for Tuberculosis Detection Using Deep Learning,” IEEE Access, vol. 8, pp. 217897- 217907, 2020. Crossref, https://doi.org/10.1109/ACCESS.2020.3041867
[8] Michail Mamalakis et al., “DenResCov-19: A Deep Transfer Learning Network for Robust Automatic Classification of COVID-19, Pneumonia, and Tuberculosis from X-Rays,” Computerized Medical Imaging and Graphics, vol. 94, p.102008, 2021. Crossref, https://doi.org/10.1016/j.compmedimag.2021.102008
[9] ParveenChandna, Anju Dhiman, and Dalip Singh Dhiman, "HRCT Imaging of Pulmonary Tuberculosis," SSRG International Journal of Medical Science, vol. 9, no. 5, pp. 1-12, 2022. Crossref, https://doi.org/10.14445/23939117/IJMS-V9I5P101
[10] Thi Kieu Khanh Ho et al., “Utilizing Pretrained Deep Learning Models for Automated Pulmonary Tuberculosis Detection using Chest Radiography,” Asian Conference on Intelligent Information and Database Systems, Springer, Cham, pp. 395-403, 2021. Crossref, https://doi.org/10.1007/978-3-030-14802-7_34
[11] Quang H. Nguyen et al., “Deep Learning Models for Tuberculosis Detection from Chest X-Ray Images,” 2019 26th International Conference on Telecommunications (ICT), IEEE, pp. 381-385, 2019. Crossref, https://doi.org/10.1109/ICT.2019.8798798
[12] Muhammad Ayaz, Furqan Shaukat, and Gulistan Raja, “Ensemble Learning based Automatic Detection of Tuberculosis in Chest X-Ray Images Using Hybrid Feature Descriptors,” Physical and Engineering Sciences in Medicine, vol. 44, no. 1, pp. 183-194, 2021. Crossref, https://doi.org/10.1007/s13246-020-00966-0
[13] G Jignesh Chowdary et al., Class Dependency based Learning using Bi-LSTM Coupled with the Transfer Learning of VGG16 for the Diagnosis of Tuberculosis from Chest X-Rays,” arXiv preprint arXiv:2108.04329, 2021. Crossref, https://doi.org/10.48550/arXiv.2108.04329
[14] Ray-I Chang, Yu-Hsuan Chiu, and Jeng-Wei Lin, “Two-Stage Classification of Tuberculosis Culture Diagnosis using Convolutional Neural Network with Transfer Learning,” The Journal of Supercomputing, vol. 76, no. 11, pp. 8641-8656, 2020. Crossref, https://doi.org/10.1007/s11227-020-03152-x
[15] J. Anto Germin Sweeta, and Dr. B. Sivagami, "Contemporary Techniques in Digital Image Processing," SSRG International Journal of Computer Science and Engineering, vol. 6, no. 11, pp. 43-46, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I11P109
[16] Mustapha Oloko-Oba, and Serestina Viriri, “Pre-Trained Convolutional Neural Network for the Diagnosis of Tuberculosis.” International Symposium on Visual Computing, Springer, Cham, pp. 558-569, 2020. Crossref, https://doi.org/10.1007/978-3-030-64559-5_44
[17] Yibin Wang, Haifeng Wang, and Zhaohua Peng, “Rice Diseases Detection and Classification using Attention based Neural Network and Bayesian Optimization,” Expert Systems with Applications, vol. 178, p. 114770, 2021. Crossref, https://doi.org/10.1016/j.eswa.2021.114770
[18] K. Aravinth Raaj, B. Joel Sherwin, and S. Sabeetha Saraswathi, "Automated Detection of Abnormalities in Chest X-RayImages Using Convolutional Neural Networks," International Journal of P2P Network Trends and Technology, vol. 8, no. 2, pp. 18-24, 2018. Crossref, https://doi.org/10.14445/22492615/IJPTT-V8I2P404
[19] Maryam Rahimzad et al., “An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE),” Remote Sensing, vol. 13, no. 13, p. 2501, 2021. Crossref, https://doi.org/10.3390/rs13132501
[20] [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset
[21] Moumen El-Melegy, Doaa Mohamed, and Tarek ElMelegy, “Automatic Detection of Tuberculosis Bacilli from Microscopic Sputum Smear Images Using Faster R-CNN, Transfer Learning and Augmentation,” Iberian Conference on Pattern Recognition and Image Analysis, Springer, Cham, pp. 270-278, 2019. Crossref, https://doi.org/10.1007/978-3-030-31332-6_24
[22] Vinayakumar Ravi, Harini Narasimhan, and Tuan D. Pham, “EfficientNet-Based Convolutional Neural Networks for Tuberculosis Classification,” Advances in Artificial Intelligence, Computation, and Data Science, Springer, Cham, pp. 227-244, 2021. Crossref, https://doi.org/10.1007/978-3-030-69951-2_9
[23] Haval T. Sadeeq et al., “A New Hybrid Method for Global Optimization Based on the Bird Mating Optimizer and the Differential Evolution,” 2021 7th International Engineering Conference “Research & Innovation amid Global Pandemic (IEC), IEEE, pp. 54-60, 2021. Crossref, https://doi.org/10.1109/IEC52205.2021.947614