Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images
DOI:
https://doi.org/10.37385/jaets.v4i2.2163Keywords:
Covid-19, Deep Learning, Convolutional Block Attention Module, Computed Tomography, Spatial Wise Attention Module, Channel Wise Attention ModuleAbstract
with the help of effective DL(Deep Learning) based algorithms. Though several clinical procedures and imaging modalities exists to diagnose Covid-19, these methods are time-consuming processes and sometimes the predictions are incorrect. Concurrently, AI (Artificial Intelligence) based DL models have gained attention in this area due to its innate capability for efficient learning. Though conventional systems have tried to perform better prediction, they lacked in accuracy with prediction rate. Moreover, the conventional systems have not utilized attention model completely for Covid-19 detection. This research is intended to resolve these pitfalls of covid-19 detection methods with the help of deep feature wise attention based Convolutional Neural Network. For this purpose, the data has been pre-processed by image resizing, the Residual Descriptor with Conv-BAM(Convolutional Block Attention Module) has been employed to obtain refined features from spatial and channel wise attention based module. The obtained features are used in the present study to improvise the classification as covid positive or negative. The performance of the proposed system has been assessed with regard to metrics to prove better efficiency. The proposed method achieved high accuracy rate of 97.82%. This DL based model can be used as a supplementary tool in the diagnosis of Covid-19 alongside other diagnostic method
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Addepalli Lavanya, L. G. (2023). Assessing the Performance of Python Data Visualization Libraries: A Review. International Journal of Computer Engineering in Research Trends, 26-39.
Afif, M. A. (2023). Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction. Multimedia Tools and Applications, 1-15.
Ahuja, S. P. (2021). Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Applied Intelligence, 51(1), 571-585.
Alom, M. R. (2020). COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches. COVID_MTNet: .
Alshazly, H. L. (2021). COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. PeerJ Computer Science, 7, e655.
Aslani, S. &. (2023). Utilisation of deep learning for COVID-19 diagnosis. Clinical Radiology, 78(2), 150-157.
Benmalek, E. E. (2021). Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis. Biomedical Engineering Advances, 1, 100003.
Bhatnagar, V. P. (2021). Descriptive analysis of COVID-19 patients in the context of India. Journal of Interdisciplinary Mathematics, 24(3), 489-504.
Chen, Y. L. (2022). Classification of lungs infected COVID-19 images based on inception-ResNet. Computer methods and programs in biomedicine, 225, 107053.
Cifci, M. A. (2020). Deep learning model for diagnosis of corona virus disease from CT images. Int. J. Sci. Eng. Res, 11(4), 273-278.
Covid-19. (2023). Covid Data Tracker Weekly Review. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html.
Fan, X. F. (2022). COVID-19 CT image recognition algorithm based on transformer and CNN. Displays, 102150.
Ghoshal, B. &. (2020). Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection.
Gozes, O. F.-A. (2020). Coronavirus detection and analysis on chest ct with deep learning. . arXiv preprint arXiv:2004., 02640.
Halder, A. &. (2021). COVID-19 detection from lung CT-scan images using transfer learning approach. Machine Learning: Science and Technology, 2(4), 045013.
He, X. W. (2020). Benchmarking deep learning models and automated model design for COVID-19 detection with chest CT scans. MedRxiv, 2020.2006. 2008., 20125963. .
He, X. Y. (2020). Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. medrxiv, 2020.2004. 2013., 20063941.
Jaiswal, A. G. (2021). Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics, 39(15), 5682-5689.
Jin, C. C. (2020). Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature communications, 11(1), 5088.
Kollias, D. A. (2022). AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging.
Krishnaswamy Rangarajan, A. &. (2022). A fused lightweight CNN model for the diagnosis of COVID-19 using CT scan images. Automatika: ?asopis za automatiku, mjerenje, elektroniku, ra?unarstvo i komunikacije, 63(1), 171-184.
Kumar, A. G. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 569-573.
Kumar, A. G. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 569-573.
Li, T. W. (2021). Computer-aided diagnosis of COVID-19 CT scans based on spatiotemporal information fusion. Journal of healthcare engineering.
Li, X. T. (2021.). Classification of COVID-19 chest CT images based on ensemble deep learning. Journal of healthcare engineering.
Maghdid, H. S. ( 2021). Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. Paper presented at the Multimodal image exploitation and learning.
Maloth, B. S. (2012). Non linear programming computation outsourcing in the cloud. Int. J. Comput. Sci. Eng. Technol, 1-10.
Mantas, J. (2020). Setting up an easy-to-use machine learning pipeline for medical decision support: a case study for COVID-19 diagnosis based on deep learning with CT scans. Public Health Dur. Pandemic, 272, 13.
Mercaldo, F. B. (2023). Coronavirus covid-19 detection by means of explainable deep learning. Scientific Reports, 13(1), 462.
Panwar, H. G.-M. (2020). Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons & Fractals, 138, 109944.
Pathan, S. S. (2021). Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images. Computers in Biology and Medicine, 137, 104835.
Polsinelli, M. C. (2020). A light CNN for detecting COVID-19 from CT scans of the chest. . Pattern recognition letters, 140, 95-100. .
Pydala, B. K. (2023). Smart_Eye: A Navigation and Obstacle Detection for Visually Impaired People through Smart App. Journal of Applied Engineering and Technological Science (JAETS), 992–1011.
Rohila, V. S. (2021). Deep learning assisted COVID-19 detection using full CT-scans. Internet of Things, 14, 100377.
Rudra Kumar, M. P. (2022). Diagnosis and Medicine Prediction for COVID-19 Using Machine Learning Approach. In Computational Intelligence in Machine Learning: Select Proceedings of ICCIML 2021;Singapore: Springer Nature Singapore., 123-133.
Saiz, F. &. (2020). OVID-19 detection in chest X-ray images using a deep learning approach. .
Serte, S. &. (2021). Deep learning for diagnosis of COVID-19 using 3D CT scans. . Computers in biology and medicine, 132, 104306.
Shah, V. K. (2021). Diagnosis of COVID-19 using CT scan images and deep learning techniques. Emergency radiology, 28, 497-505.
Shambhu, S. K. (2021). Binary classification of covid-19 ct images using cnn: Covid diagnosis using ct. International Journal of E-Health and Medical Communications (IJEHMC), 13(2), 1-13.
Shambhu, S. K. (2021). Binary classification of covid-19 ct images using cnn: Covid diagnosis using ct. . International Journal of E-Health and Medical Communications (IJEHMC), 13(2), 1-13.
Silva, P. L. (2020). COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis. Informatics in Medicine Unlocked, 20, , 100427.
Soares, E. A. (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. medrxiv, 2020.2004. 2024.20078584.
Sumana Sikdar, P. S. (2021). Evaluation of State-Wise Epidemiological Outbreak of COVID-19 Cases In India by Data Analysis Approach to Forecast the Coronavirus Disease Pandemic. International Journal of Computer Engineering in Research Trends, 50-56.
Sushma Jaiswal, P. G. (2023). Ensemble based Model for Diabetes Prediction and COVID-19 Mortality Risk Assessment in Diabetic Patients . International Journal of Computer Engineering in Research Trends, 99-106.
Wang, W. X. (2020). Detection of SARS-CoV-2 in different types of clinical specimens. . Jama, 323(18), , 1843-1844.
Xu, M. W. (2020). COVID?19 diagnostic testing: technology perspective. Clinical and translational medicine, 10(4), 158.
Zhang, K. L. (2020). Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 181(6), 1423-1433. , e1411.
Zheng, C. D. (2020). Deep learning-based detection for COVID-19 from chest CT using weak label. medrxiv, 2020.2003. 2012. , 20027185.