Robust Medical X-Ray Image Classification by Deep Learning with Multi-Versus Optimizer
Received: 16 June 2023 | Revised: 24 June 2023 | Accepted: 29 June 2023 | Online: 9 August 2023
Corresponding author: Ramasamy Ponnusamy
Abstract
Classification of medical images plays an indispensable role in medical treatment and training tasks. Much effort and time are required in the extraction and selection of classification features of medical images. Deep Neural Networks (DNNs) are an evolving Machine Learning (ML) method that has proved its ability in various classification tasks. Convolutional Neural Networks (CNNs) present the optimal results for changing image classification tasks. In this regard, this study focused on developing a Multi-versus Optimizer with Deep Learning Enabled Robust Medical X-ray Image Classification (MVODL-RMXIC) method, aiming to identify abnormalities in medical X-ray images. The MVODL-RMXIC model used the Cross Bilateral Filtering (CBF) technique for noise removal, a MixNet feature extractor with an MVO algorithm based on hyperparameter optimization, and Bidirectional Long-Short-Term Memory (BiLSTM) for image classification. The proposed MVODL-RMXIC model was simulated and evaluated, showing its efficiency over other current methods.
Keywords:
medical x-ray images, biomedical imaging, image classification, deep learning, multi-versus optimizerDownloads
References
J. I. Z. Chen, "Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach," Journal of IoT in Social, Mobile, Analytics, and Cloud, vol. 3, no. 2, pp. 132–148, Jun. 2021.
S. Chakraborty, S. Paul, and K. M. A. Hasan, "A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification," SN Computer Science, vol. 3, no. 1, Oct. 2021, Art. no. 17.
U. Subramaniam, M. M. Subashini, D. Almakhles, A. Karthick, and S. Manoharan, "An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques," BioMed Research International, vol. 2021, Nov. 2021, Art. no. e1896762.
R. Kumar et al., "Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifier." 2020.
A. A. Reshi et al., "An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification," Complexity, vol. 2021, May 2021, Art. no. e6621607.
P. K. Mall and P. K. Singh, "BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images," International Journal of System Assurance Engineering and Management, vol. 13, no. 1, pp. 658–672, Mar. 2022.
K. KC, Z. Yin, M. Wu, and Z. Wu, "Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images," Signal, Image and Video Processing, vol. 15, no. 5, pp. 959–966, Jul. 2021.
H. Munusamy, K. J. Muthukumar, S. Gnanaprakasam, T. R. Shanmugakani, and A. Sekar, "FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation," Biocybernetics and Biomedical Engineering, vol. 41, no. 3, pp. 1025–1038, Jul. 2021.
R. Kumar et al., "Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient," Multimedia Tools and Applications, vol. 81, no. 19, pp. 27631–27655, Aug. 2022.
I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 635–640, Jun. 2020.
T. Akhtar, N. G. Haider, and S. M. Khan, "A Comparative Study of the Application of Glowworm Swarm Optimization Algorithm with other Nature-Inspired Algorithms in the Network Load Balancing Problem," Engineering, Technology & Applied Science Research, vol. 12, no. 4, pp. 8777–8784, Aug. 2022.
T. N. Le, H. M. V. Nguyen, T. A. Nguyen, T. T. Phung, and B. D. Phan, "Optimization of Load Ranking and Load Shedding in a Power System Using the Improved AHP Algorithm," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8512–8519, Jun. 2022.
M. Bhalekar and M. Bedekar, "D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8366–8373, Apr. 2022.
O. Yadav, K. Passi, and C. K. Jain, "Using Deep Learning to Classify X-ray Images of Potential Tuberculosis Patients," in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, Sep. 2018, pp. 2368–2375.
S. Karakanis and G. Leontidis, "Lightweight deep learning models for detecting COVID-19 from chest X-ray images," Computers in Biology and Medicine, vol. 130, Mar. 2021, Art. no. 104181.
N.-A.- Alam, M. Ahsan, M. A. Based, J. Haider, and M. Kowalski, "COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning," Sensors, vol. 21, no. 4, Jan. 2021, Art. no. 1480.
K. Shirai, K. Sugimoto, and S. Kamata, "Adjoint Bilateral Filter and Its Application to Optimization-based Image Processing," APSIPA Transactions on Signal and Information Processing, vol. 11, no. 1, Apr. 2022.
J. Ou and H. Wu, "Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping," in Pattern Recognition and Computer Vision, Nanjing, China, 2020, pp. 626–638.
R. M. A. Ikram, H. L. Dai, A. A. Ewees, J. Shiri, O. Kisi, and M. Zounemat-Kermani, "Application of improved version of multi verse optimizer algorithm for modeling solar radiation," Energy Reports, vol. 8, pp. 12063–12080, Nov. 2022.
X. Liu, S. Liu, X. Li, B. Zhang, C. Yue, and S. Y. Liang, "Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network," Journal of Manufacturing Systems, vol. 60, pp. 608–619, Jul. 2021.
"COVID-19 Radiography Database." https://www.kaggle.com/datasets/
tawsifurrahman/covid19-radiography-database.
S. H. Khan, A. Sohail, A. Khan, and Y.-S. Lee, "COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN," Diagnostics, vol. 12, no. 2, Feb. 2022, Art. no. 267.
M. Ragab, S. Alshehri, N. A. Alhakamy, W. Alsaggaf, H. A. Alhadrami, and J. Alyami, "Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification," Journal of Healthcare Engineering, vol. 2022, Mar. 2022, Art. no. e6074538.
Downloads
How to Cite
License
Copyright (c) 2023 Thirugnanam Kumar, Ramasamy Ponnusamy
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
