Abstract
The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset’s key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM’s parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Shaik NS, Cherukuri TK: Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans. Computers in Biology and Medicine 141: 105127, 2022
Suganya D, Kalpana R: Prognosticating various acute covid lung disorders from COVID-19 patient using chest CT Images. Engineering Applications of Artificial Intelligence 105820, 2023
Sedik A, Hammad M, El-Samie A, Fathi E, Gupta B.B, El-Latif A, Ahmed A: Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Computing and Applications 1–18, 2021
Bao G, Chen H, Liu T, Gong G, Yin Y, Wang L, Wang X: COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recognition 124:108499, 2022
Huang Z, Liu X, Wang R, Zhang M, Zeng X, Liu J, Yang Y, Liu X, Zheng H, Liang D, Hu Z: FaNet. fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms. Applied Intelligence 51(5): 2838–2849, 2021
Li K, Liu X, Yip R, Yankelevitz DF, Henschke CI, Geng Y, Fang Y, Li W, Pan C, Chen X, Qin P: Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging. Clinical Imaging 78:223-229,2021
Mandal S: Identification of Severity of Infection for COVID-19 Affected Lungs Images using Elephant Swarm Water Search Algorithm. International Journal of Modelling and Simulation 42(3):518-532, 2022
Zhou T, Lu H, Yang Z, Qiu S, Huo B, Dong Y: The ensemble deep learning model for novel COVID-19 on CT images. Applied soft computing 98:106885, 2021
Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Bhardwaj P, Singh V: A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos, Solitons & Fractals 140: 110190, 2020
Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Wang R, Zhao H, Chong Y, Shen J: Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM transactions on computational biology and bioinformatics 18(6):2775-2780, 2021
Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS: Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cognitive Computation 1–13, 2021
Bhardwaj P, Kaur A: A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality. International Journal of Imaging Systems and Technology 31(4): 1775-1791, 2021
Islam MR, Nahiduzzaman M: Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach. Expert Systems with Applications 195: 116554, 2022
Prabha B, Kaur S, Singh J, Nandankar P, Jain SK, Pallathadka H: Intelligent predictions of Covid disease based on lung CT images using machine learning strategy. Materials Today: Proceedings 2021
Islam MZ, Islam MM, Asraf A: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in medicine unlocked 20: 100412,2020
Gaur P, Malaviya V, Gupta A, Bhatia G, Pachori RB, Sharma D: COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning. Biomedical Signal Processing and Control 71: p.103076, 2022
Thakur S, Kumar A: X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN). Biomedical Signal Processing and Control 69: 102920, 2021
Abbasi WA, Abbas SA, Andleeb S, Ul Islam G, Ajaz SA, Arshad K, Khalil S, Anjam A, Ilyas K, Saleem M, Chughtai J: COVIDC. An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology. Informatics in Medicine Unlocked 23:100540, 2021
Ahuja S, Panigrahi BK, Dey N, Taneja A, Gandhi TK: McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices. Applied Soft Computing 131: 109683,2022
Liu X, Gao K, Liu B, Pan C, Liang K, Yan L, Ma J, He F, Zhang S, Pan S, Yu Y: Advances in deep learning-based medical image analysis. Health Data Science 2021.
Ahmed S, Hossain T, Hoque OB, Sarker S, Rahman S, Shah FM: Automated covid-19 detection from chest x-ray images: a high-resolution network (hrnet) approach. SN computer science 2(4): 1-17,2021
Goyal S, Singh R: Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing 1–21, 2021
Trojovský P, Dehghani M: Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors 22(3):855, 2022
Aziz AZ B: CT scans for covid-19 classification. Kaggle. Retrieved January 24, 2023, from https://www.kaggle.com/datasets/azaemon/preprocessed-ct-scans-for-covid19 2020, August 11
PlamenEduardo. SARS-COV-2 CT-scan dataset. Kaggle. Retrieved January 24, 2023, from https://www.kaggle.com/datasets/plameneduardo/sarscov2-ctscan-dataset?select=COVID 2020, May 30
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This work is funded by ASEAN-India Collaborative R&D scheme under ASEAN-India S&T Development Fund (AISTDF), grant number CRD/2021/000483.
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Vinothini, R., Niranjana, G. & Yakub, F. A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images. J Digit Imaging 36, 2480–2493 (2023). https://doi.org/10.1007/s10278-023-00852-7
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DOI: https://doi.org/10.1007/s10278-023-00852-7