Computer-Aided Diagnosis Model for Lung Diseases Using Sea Lion Optimization with Deep Convolutional Recurrent Neural Network on Chest X-ray Images
Computer-Aided Diagnosis Model for Lung Diseases Using Sea Lion Optimization with Deep Convolutional Recurrent Neural Network on Chest X-ray Images |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-10 |
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| Year of Publication : 2023 | ||
| Author : S. Vidyasri, S. Saravanan |
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| DOI : 10.14445/22315381/IJETT-V71I10P206 | ||
How to Cite?
S. Vidyasri, S. Saravanan, "Computer-Aided Diagnosis Model for Lung Diseases Using Sea Lion Optimization with Deep Convolutional Recurrent Neural Network on Chest X-ray Images," International Journal of Engineering Trends and Technology, vol. 71, no. 10, pp. 60-70, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I10P206
Abstract
Lung Disease (LD) is the leading factor of increasing death rates across the world and incorporates tuberculosis diseases, pneumonia, COVID-19, and pneumothorax. Prompt and early diagnosis of LD can probably reduce the risk of death and improve the patient's quality of life. Current medical image modalities and imaging tests seem to be effective tools that can help medical practitioners detect different conditions. Computed Tomography (CT) and Chest X-ray (CXR) radiographic images usually use image modalities. These diagnostic tools allow clinicians to look at the internal structure of the body without the need for cutting. Recently, Convolutional Neural Networks (CNNs) have become the potential technique of Computer Vision (CV) and have reached promising outcomes in medical image diagnosis. This study designs an Automated Lung Disease Detection Using Sea Lion Optimization with Deep Convolutional Recurrent Neural Network (SLO-DCRNN) technique on CXR images. In the presented SLO-DCRNN model, the DL and hyperparameter tuning process can be employed for automated LD. At the initial stage, the SLO-DCRNN model uses the adaptive Weiner Filter (AWF) technique to eliminate the noise level that exists in the images. Next, the SLO-DCRNN method exploits the Neural Architectural Search Network (NASNet) Large model for feature vector generation. Followed by the DCRNN approach is utilized to identify different kinds of LDs. At last, the SLO system was enforced for the tuning process of the DCRNN approach. An extensive set of investigations was performed to demonstrate the superior result of the SLO-DCRNN technique. The simulation results ensured the improvement of the SLO-DCRNN technique over other existing systems.
Keywords
Lung Diseases, Deep learning, Medical imaging, Sealion optimizer, Chest X-ray images.
References
[1] Vinayakumar Ravi et al., “Deep Learning-Based Meta-Classifier Approach for COVID-19 Classification using CT Scan and Images,” Multimedia Systems, vol. 28, no. 4, pp. 1401-1415, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Georgios Petmezas et al., “Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function,” Sensors, vol. 22, no. 3, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shelly Soffer et al., “Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review,” Academic Radiology, vol. 29, pp. S226-S235, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Mukesh Soni et al., “Hybridizing Convolutional Neural Network for Classification of Lung Diseases,” International Journal of Swarm Intelligence Research, vol. 13, no. 2, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sungyeup Kim et al., “Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-Ray Images,” Diagnostics, vol. 12, no. 4, pp. 1-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Priya Aggarwal et al., “COVID-19 Image Classification using Deep Learning: Advances, Challenges, and Opportunities,” Computers in Biology and Medicine, vol. 144, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Gopi Kasinathan, and Selvakumar Jayakumar, “Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques,” BioMed Research International, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jiaxing Sun et al., “Detection and Staging of Chronic Obstructive Pulmonary Disease using a Computed Tomography–Based Weakly Supervised Deep Learning Approach,” European Radiology, vol. 32, no. 8, pp. 5319-5329, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Eduarda M. Bortoluzzi et al., “Image Classification and Automated Machine Learning to Classify Lung Pathologies in Deceased Feedlot Cattle,” Veterinary Sciences, vol. 10, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] F.M. Javed Mehedi Shamrat et al., “LungNet22: A Fine-Tuned Model for Multi-class Classification and Prediction of Lung Disease Using X-ray Images,” Journal of Personalized Medicine, vol. 12, no. 5, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Thavavel Vaiyapuri et al., “Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images,” Applied Sciences, vol. 12, no. 11, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] S. Farjana Farvin, and S. Krishna Mohan, “A Comparative Study on Lung Cancer Detection using Deep Learning Algorithms,” SSRG International Journal of Computer Science and Engineering, vol. 9, no. 5, pp. 1-4, 2022.
[CrossRef] [Publisher Link]
[13] Huiling Lu, “Computer-Aided Diagnosis Research of a Lung Tumor Based on a Deep Convolutional Neural Network and Global Features,” BioMed Research International, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Feng Yang et al., “Annotations of Lung Abnormalities in the Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases,” Data, vol. 7, no. 7, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] K. Aravinth Raaj et al., “Automated Detection of Abnormalities in Chest X-Ray Images Using Convolutional Neural Networks,” International Journal of P2P Network Trends and Technology, vol. 8, no. 2, pp. 18-24, 2018.
[Publisher Link]
[16] C.S. Retmin Raj et al., “A Novel Feature-Significance-Based K-Nearest Neighbor Classification Approach for Computer-Aided Diagnosis of Lung Disorders,” Current Medical Imaging, vol. 14, no. 2, pp. 289-300, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] J. Dhalia Sweetlin, H. Khanna Nehemiah, and A. Kannan, “Computer-Aided Diagnosis of Drug-Sensitive Pulmonary Tuberculosis with Cavities, Consolidations, and Nodular Manifestations on Lung CT Images,” International Journal of Bio-Inspired Computation, vol. 13, no. 2, pp. 71-85, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] E. Mique Jr, and A. Malicdem, “Deep Residual U-Net-Based Lung Image Segmentation for Lung Disease Detection,” IOP Conference Series: Materials Science and Engineering, vol. 803, no. 1, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Hadi Salehi et al., “A SAR Image Despeckling Method based on an Extended Adaptive Wiener Filter and Extended Guided Filter,” Remote Sensing, vol. 12, no. 15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Adedamola O. Adedoja et al., “Intelligent Mobile Plant Disease Diagnostic System Using NASNet-Mobile Deep Learning,” IAENG International Journal of Computer Science, vol. 49, no. 1, pp. 216-231, 2022.
[Google Scholar] [Publisher Link]
[21] Binh Minh Nguyen et al., “An Improved Sea Lion Optimization for Workload Elasticity Prediction with Neural Networks,” International Journal of Computational Intelligence Systems, vol. 15, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Emre Çakır et al., “Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 6, pp. 1291-1303, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[23] COVID-19 Radiography Database. [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database/code
[24] Tripti Goel et al., “OptCoNet: an Optimized Convolutional Neural Network for an Automatic Diagnosis of COVID-19,” Applied Intelligence, vol. 51, pp. 1351-1366, 2021.
[CrossRef] [Google Scholar] [Publisher Link]