Abstract
The SARS-CoV-2 (severe acute respiratory syndrome coronavirus) pandemic, also known as COVID-19 (coronavirus 2019), impacted humanity worldwide and significantly impacted the healthcare community. COVID-19 infection and transmission have resulted in several international issues, including health hazards. Sore throat, trouble breathing, cough, fever, weariness, and other clinical signs have been described. In SARS-CoV-2 patients, the most common infections are in the lungs and the gastric intestine. Lung infections may be caused by viral or bacterial infections, physical trauma, or inhalation of harmful particles. This research presents deep learning-based approaches for COVID-19 infection detection based on radiological images, prevention and therapy based on benchmark publicly available datasets. Finally, the analysis and findings explore evidence-based methodologies and modalities, leading to a conclusion and possible future healthcare planning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rehman, A., Sadad, T., Saba, T., Hussain A., Tariq, U.: Real-time diagnosis system of COVID-19 using X-ray images and deep learning. IEEE IT Prof. 23(4), 57–62 (2021). https://doi.org/10.1109/MITP.2020.3042379
Saba, T., Abunadi, I., Shahzad, M.N., Khan, A.R.: Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc. Res. Tech. 84(7), 1462–1474 (2021)
Khan, M.A., Kadry, S., Zhang, Y.D., Akram, T., Sharif, M., et al.: Prediction of COVID-19-pneumonia based on selected deep features and one class kernel extreme learning machine. Comput. Electr. Eng. 90, 106960 (2021)
Amin, J., Anjum, M.A., Sharif, M., Rehman, A., Saba, T., Zahra, R.: Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network. Microsc. Res. Tech. https://doi.org/10.1002/jemt.23913
Haimed, A.M.A., Saba, T., Albasha, A., Rehman, A., Kolivand, M.: Viral reverse engineering using artificial intelligence and big data COVID-19 infection with long short-term memory (LSTM). Environ. Technol. Innov. 22, 1–20 (2021)
Khan, M.Z., Khan, M.U.G., Saba, T., Razzak, I., Rehman, A., Bahaj, S.A.: Hot-Spot zone detection to tackle COVID19 spread by fusing the traditional machine learning and deep learning approaches of computer vision. IEEE Access 9, 100040–100049 (2021)
Rehman, A., Saba, T., Tariq, U., Ayesha, N.: Deep learning-based COVID-19 detection using CT and X-ray images: current analytics and comparisons. IT Prof. 23(3), 63–68 (2021)
Dawood, S., Dawood, A., Alaskar, H., Saba, T.: COVID-19 artificial intelligence based surveillance applications in the kingdom of Saudi Arabia. In: 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), pp. 200–205. IEEE (2021)
Saba, T., and Khan, A. R. (Eds.). (2021). Intelligent computing applications for COVID-19: predictions, diagnosis, and prevention. CRC Press.
Neamah, K., Mohamad, D., Saba, T., Rehman, A.: Discriminative features mining for offline handwritten signature verification. 3D Res. 5(2), 1–6. https://doi.org/10.1007/s13319-013-0002-3
Ramzan, F., Khan, M.U.G., Iqbal, S., Saba, T., Rehman, A.: Volumetric segmentation of brain regions from MRI scans using 3D convolutional neural networks. IEEE Access 8, 103697–103709 (2020)
Rahim, M.S.M., Norouzi, A., Rehman, A., Saba, T.: 3D bones segmentation based on CT images visualization. Biomed. Res. 28(8), 3641–3644 (2017)
Rashid, M., Khan, M.A., Alhaisoni, M., Wang, S.H., Naqvi, S.R., et al.: A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection. Sustainability 12(12), 5037 (2020)
Rehman, A., Khan, M.A., Saba, T., Mehmood, Z., Tariq, U., Ayesha, N.: Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc. Res. Tech. 84(1), 133–149 (2021). https://doi.org/10.1002/jemt.23597
Sadad, T., Khan, A.R., Hussain, A., Tariq, U., Fati, S.M., Bahaj, S.A., Munir, A.: Internet of medical things embedding deep learning with data augmentation for mammogram density classification. Microsc. Res. Tech. (2021)
Rehman, A., Abbas, N., Saba, T., Mehmood, Z., Mahmood, T., Ahmed, K.T.: Microscopic malaria parasitemia diagnosis and grading on benchmark datasets. Microsc. Res. Tech. 81(9), 1042–1058 (2018). https://doi.org/10.1002/jemt.23071
Rehman, A., Saba, T.: (2011) Performance analysis of character segmentation approach for cursive script recognition on benchmark database. Digit. Sig. Proc. 21(3), 486–490 (2021). https://doi.org/10.1016/j.dsp.2011.01.016
Ahlawat, S., Sharma, K.K.: Immunological co-ordination between gut and lungs in SARS-CoV-2 infection. Virus Res. 198103 (2020)
Polidoro, R.B., Hagan, R.S., de Santis Santiago, R., Schmidt, N.W.: Overview: Systemic inflammatory response derived from lung injury caused by SARS-CoV-2 infection explains severe outcomes in COVID-19. Front. Immunol. 11, 1626 (2020)
Mayor-Ibarguren, A., Robles-Marhuenda, Á.: A hypothesis for the possible role of zinc in the immunological pathways related to COVID-19 Infection. Front. Immunol. 11, 1736 (2020)
Rehman, A., Alqahtani, S., Altameem, A., Saba, T.: Virtual machine security challenges: case studies. Int. J. Mach. Learn. Cybern. 5(5), 729–742 (2014)
Alyami, J., Khan, A.R., Bahaj, S.A., Fati, S.M.: Microscopic handcrafted features selection from computed tomography scans for early stage lungs cancer diagnosis using hybrid classifiers. Microsc. Res. Tech.
Convissar, D., Gibson, L.E., Berra, L., Bittner, E.A., Chang, M.G.: Application of lung ultrasound during the coronavirus disease 2019 pandemic: a narrative review. Anesth. Analg.
Zhou, S., Wang, Y., Zhu, T., Xia, L.: CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan China. Am. J. Roentgenol. 214(6), 1287–1294 (2020)
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z.A., Jacobi, A.: CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295(1), 202–207 (2020)
Buonsenso, D., Raffaelli, F., Tamburrini, E., Biasucci, D.G., Salvi, S., Smargiassi, A., Inchingolo, R., Scambia, G., Lanzone, A., Testa, A.C., Moro, F.: Clinical role of lung ultrasound for the diagnosis and monitoring of COVID-19 pneumonia in pregnant women. Ultrasound Obstet. Gynecol. (2020)
Abbas, N., Saba, T., Mohamad, D., Rehman, A., Almazyad, A.S., Al-Ghamdi, J.S.: Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears. Neural Comput. Appl. 29(3), 803–818 (2018)
Amin, J., Sharif, M., Raza, M., Saba, T., Rehman, A.: Brain tumor classification: feature fusion. In: 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1–6. IEEE (2019)
Iqbal, S., Khan,M.U.G., Saba, T. Mehmood, Z. Javaid,N., Rehman,A., Abbasi, R.: Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation. Microsc. Res. Tech. 82(8), 1302–1315 (2019). https://doi.org/10.1002/jemt.23281
Park, J.Y., Lee, Y.J., Kim, T., Lee, C.Y., Kim, H.I., Kim, J.H., Park, S., Hwang, Y.I., Jung, K.S., Jang, S.H.: Collateral effects of the coronavirus disease 2019 pandemic on lung cancer diagnosis in Korea. BMC Cancer 20(1), 1–8 (2020)
Cruces, P., Retamal, J., Hurtado, D.E., Erranz, B., Iturrieta, P., González, C., Díaz, F.: A physiological approach to understand the role of respiratory effort in the progression of lung injury in SARS-CoV-2 infection. Crit. Care 24(1), 1–10 (2020)
Jacobi, A., Chung, M., Bernheim, A., Eber, C.: Portable chest X-ray in coronavirus disease-19 (COVID-19): a pictorial review. Clin. Imaging
Bao, L., Zhang, C., Dong, J., Zhao, L., Li, Y., Sun, J.: Oral microbiome and SARS-CoV-2: beware of lung co-infection. Front. Microbiol. 11, 1840 (2020)
Musolino, A.M., Supino, M.C., Buonsenso, D., Ferro, V., Valentini, P., Magistrelli, A., Lombardi, M.H., Romani, L., D'Argenio, P., Campana, A.: Lung ultrasound in children with COVID-19: preliminary findings. Ultrasound Med. Biol.
Nieman, G.F., Gatto, L.A., Andrews, P., Satalin, J., Camporota, L., Daxon, B., Blair, S.J., Al-Khalisy, H., Madden, M., Kollisch-Singule, M., Aiash, H.: Prevention and treatment of acute lung injury with time-controlled adaptive ventilation: physiologically informed modification of airway pressure release ventilation. Ann. Intensive Care 10(1), 1–16 (2020)
Nawaz, M., Mehmood, Z., Nazir, T., Naqvi, R.A., Rehman, A., Iqbal, M., Saba, T.: Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering. Microsc. Res. Tech. 85(1), 339–351 (2022)
Rehman, A.: Ulcer recognition based on 6-Layers deep convolutional neural network. In: Proceedings of the 2020, 9th international conference on software and information engineering (ICSIE), pp. 97–101. Cairo Egypt (2020)
Safdar, A., Khan, M.A., Shah, J.H., Sharif, M., Saba, T., Rehman, A., Javed, K., Khan, J.A.: Intelligent microscopic approach for identification and recognition of citrus deformities. Microsc. Res. Tech. 82(9), 1542–1556 (2019)
Ojo, A.S., Balogun, S.A., Williams, O.T., Ojo, O.S.: Pulmonary fibrosis in COVID-19 survivors: predictive factors and risk reduction strategies. Pulm. Med 2020
Fu, L., Wang, B., Yuan, T., Chen, X., Ao, Y., Fitzpatrick, T., Li, P., Zhou, Y., Lin, Y.F., Duan, Q., Luo, G.: Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: a systematic review and meta-analysis. J. Infect.
Iftikhar, S., Fatima, K., Rehman, A., Almazyad, A.S., Saba, T.: An evolution based hybrid approach for heart diseases classification and associated risk factors identification. Biomed. Res. 28(8), 3451–3455 (2017)
Meethongjan, K., Dzulkifli, M., Rehman, A., Altameem, A., Saba, T.: An intelligent fused approach for face recognition. J. Intell. Syst. 22(2), 197–212 (2013). https://doi.org/10.1515/jisys-2013-0010
Lung, J.W.J., Salam, M.S.H., Rehman, A., Rahim, M.S.M., Saba, T.: Fuzzy phoneme classification using multi-speaker vocal tract length normalization. IETE Tech. Rev. 31(2), 128–136 (2014). https://doi.org/10.1080/02564602.2014.892669
Minucci, S.B., Heise, R.L., Reynolds, A.M.: Review of mathematical modeling of the inflammatory response in lung infections and injuries. Front. Appl. Math. Stat. 6, 36 (2020)
Khan, M.A., Sharif, M.I., Raza, M., Anjum, A., Saba, T., Shad, S.A.: Skin lesion segmentation and classification: a unified framework of deep neural network features fusion and selection. Expert Syst. e12497 (2019)
Zhang, G., Hu, C., Luo, L., Fang, F., Chen, Y., Li, J., Peng, Z., Pan, H.: Clinical features and short-term outcomes of 221 patients with COVID-19 in Wuhan, China. J. Clin. Virol. 104364 (2020)
Tian, S., Hu, W., Niu, L., Liu, H., Xu, H., Xiao, S.Y.: Pulmonary pathology of early phase 2019 novel coronavirus (COVID-19) pneumonia in two patients with lung cancer. J. Thorac. Oncol. (2020)
Müller, D., Rey, I.S., Kramer, F.: Automated chest CT image segmentation of COVID-19 Lung Infection based on 3D U-Net. arXiv preprint arXiv:2007.04774 (2020)
Javed, R., Rahim, M.S.M., Saba, T., Rehman, A.: A comparative study of features selection for skin lesion detection from dermoscopic images. Netw. Model. Anal. Health Inform. Bioinf 9(1), 1–13 (2020)
Jamal, A., Hazim Alkawaz, M., Rehman, A., Saba, T.: Retinal imaging analysis based on vessel detection. Microsc. Res. Tech. 80(7), 799–811 (2017)
Nazir, M., Khan, M.A., Saba, T., Rehman, A.: Brain tumor detection from MRI images using multi-level wavelets. In: 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1–5. IEEE (2019)
Saba, T., Bokhari, S.T.F., Sharif, M., Yasmin, M., Raza, M.: Fundus image classification methods for the detection of glaucoma: a review. Microsc. Res. Tech. (2018). https://doi.org/10.1002/jemt.23094
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)
Saba, T., Rehman, A., Sulong, G.: Improved statistical features for cursive character recognition” International Journal of Innovative Computing. Information and Control (IJICIC) 7(9), 5211–5224 (2011)
Saba, T., Haseeb, K., Ahmed, I., Rehman, A.: Secure and energy-efficient framework using Internet of Medical Things for e-healthcare. J. Infect. Public Health 13(10), 1567–1575 (2020)
Sadad, T., Munir, A., Saba, T., Hussain, A.: Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature. Journal of Computational Science 29, 34–45 (2018)
Ullah,H., Saba,T. Islam, N. Abbas, N. Rehman, A., Mehmood, Z. Anjum, A. (2019) An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection, Microscopy research and technique, 82(4), 361-372. doi.org/https://doi.org/10.1002/jemt.23178
Sharif, U., Mehmood, Z., Mahmood, T., Javid, M.A., et al.: Scene analysis and search using local features and support vector machine for effective content-based image retrieval. Artif. Intell. Rev. 52(2), 901–925 (2019)
Bar, S., Lecourtois, A., Diouf, M., Goldberg, E., Bourbon, C., Arnaud, E., ... & Gosset, P. (2020). The association of lung ultrasound images with COVID‐19 infection in an emergency room cohort. Anaesthesia.
Sajjad, M., Ramzan, F., Khan, M.U.G., Rehman, A., Kolivand, M., Fati, S.M., Bahaj, S.A.: Deep convolutional generative adversarial network for Alzheimer’s disease classification using positron emission tomography (PET) and synthetic data augmentation. Microsc. Res. Tech. 84(12), 3023–3034 (2021). https://doi.org/10.1002/jemt.23861
Youssef, A., Cavalera, M., Azzarone, C., Serra, C., Brunelli, E., Casadio, P., Pilu, G.: The use of lung ultrasound during the COVID-19 pandemic: A narrative review with specific focus on its role in pregnancy. J. Popul. Ther. Clin. Pharmacol. 27(SP1), e64–e75 (2020)
Yousaf, K., Mehmood, Z., Saba, T., Rehman, A., Munshi, A.M., Alharbey, R., Rashid, M.: Mobile-health applications for the efficient delivery of health care facility to people with dementia (PwD) and support to their carers: A survey. Biomed. Res. Int. 2019, 1–26 (2019)
Saba, T., Javed, R., Shafry, M., Rehman, A., Bahaj, S.A.: IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction. CMC-Computers, Materials & Continua 71(3), 6219–6237 (2022)
Trauer, M. M., Matthies, A., Mani, N., McDermott, C., & Jarman, R. (2020). The utility of lung ultrasound in COVID-19: A systematic scoping review. Ultrasound, 1742271X20950779.
Saleem, S., Usman, M., Saba, T., Abunadi, I., Rehman, A., et al.: Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator. CMC-Computers, Materials & Continua 70(1), 505–521 (2022)
Shahzad, M.N., Ali, H., Saba, T., Rehman, A., Kolivand, H., Bahaj, S.A.: Identifying patients with PTSD utilizing resting-state fMRI data and neural network approach. IEEE Access 9, 107941–107954 (2021)
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., ... & Shi, Y. (2020). Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655.
Sharif, M., Attique, M., Tahir, M.Z., Yasmim, M., Saba, T., Tanik, U.J.: A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition. Journal of Organizational and End User Computing (JOEUC) 32(2), 67–92 (2020)
Sharif, M. Khan, M.A Akram, T. Javed, M.Y. Saba,T. Rehman, A (2017) A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection, EURASIP Journal on Image and Video Processing 2017 (1), 89, pp.1–18
Wan, Y., Shang, J., Graham, R., Baric, R. S., & Li, F. (2020). Receptor recognition by the novel coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS coronavirus. Journal of virology, 94(7).
Sharif, M., Naz, F., Yasmin, M., Shahid, M.A., Rehman, A.: Face Recognition: A Survey. Journal of Engineering Science & Technology Review 10(2), 166–177 (2017)
Soleimanizadeh, S., Mohamad, D., Saba, T., Rehman, A. (2015) Recognition of partially occluded objects based on the three different color spaces (RGB, YCbCr, HSV) 3D Research, Vol. 6 (3), 1–10, doi. https://doi.org/10.1007/s13319-015-0052-9.
Liu, J., Zheng, X., Tong, Q., Li, W., Wang, B., Sutter, K., ... & Yang, D. (2020). Overlapping and discrete aspects of the pathology and pathogenesis of the emerging human pathogenic coronaviruses SARS‐CoV, MERS‐CoV, and 2019‐nCoV. Journal of medical virology, 92(5), 491-494.
Sulong, G., Rehman, A., Saba, T.: Improved offline connected script recognition based on hybrid strategy. Int. J. Eng. Sci. Technol. 2(6), 1603–1611 (2010)
Tahir, B., Iqbal, S., Khan, M.U.G., Saba, T., Mehmood, Z., Anjum, A., Mahmood, T.: Feature enhancement framework for brain tumor segmentation and classification. Microsc. Res. Tech. 82(6), 803–811 (2019)
Waheed, S.R., Alkawaz, M.H., Rehman, A., Almazyad, A.S., Saba, T.: Multifocus watermarking approach based on discrete cosine transform. Microsc. Res. Tech. 79(5), 431–437 (2016)
Yaseen, S., Abbas, S. M. A., Anjum, A., Saba, T., Khan, A., Malik, S. U. R., ... & Bashir, A. K. (2018). Improved generalization for secure data publishing. IEEE Access, 6, 27156-27165.
Yousaf, K., Mehmood, Z., Awan, I. A., Saba, T., Alharbey, R., Qadah, T., & Alrige, M. A. (2019). A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD). Health Care Management Science, 1–23.
Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
Sharif, M., Khan, S., Saba, T., Raza, M., & Rehman, A. (2019, April). Improved Video Stabilization using SIFT-Log Polar Technique for Unmanned Aerial Vehicles. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1–7). IEEE.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
Ausawalaithong, W., Thirach, A., Marukatat, S. and Wilaiprasitporn, T., 2018, November. Automatic lung cancer prediction from chest X-ray images using the deep learning approach. In 2018 11th Biomedical Engineering International Conference (BMEiCON) (pp. 1–5). IEEE.
https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
Shuja, J., Alanazi, E., Alasmary, W. and Alashaikh, A., 2020. COVID-19 Datasets: A Survey and Future Challenges. medRxiv.
Born, J., Brändle, G., Cossio, M., Disdier, M., Goulet, J., Roulin, J. and Wiedemann, N., 2020. POCOVID-Net: automatic detection of COVID-19 from a new lung ultrasound imaging dataset (POCUS). arXiv preprint arXiv:2004.12084.
Gao, Z., Yada, S., Wakamiya, S. and Aramaki, E., 2020. Naist covid: Multilingual COVID-19 twitter and weibo dataset. arXiv preprint arXiv:2004.08145.
Qureshi,I. Khan,MA., Sharif, M., Saba, T., Ma, J. (2020) Detection of glaucoma based on cup-to-disc ratio using fundus images International Journal of Intelligent Systems Technologies and Applications, Vol.19(1), pp.1 - 16, https://doi.org/10.1504/IJISTA.2020.105172
Hu, Y., Huang, H., Chen, A. and Mao, X.L., 2020. Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo. arXiv, pp. arXiv-2005.
Perveen, S., Shahbaz, M., Saba, T., Keshavjee, K., Rehman, A., Guergachi, A.: Handling irregularly sampled longitudinal data and prognostic modeling of diabetes using machine learning technique. IEEE Access 8, 21875–21885 (2020)
Cui, L. and Lee, D., 2020. CoAID: COVID-19 Healthcare Misinformation Dataset. arXiv preprint arXiv:2006.00885.
Zarei, K., Farahbakhsh, R., Crespi, N. and Tyson, G., 2020. A first Instagram dataset on COVID-19. arXiv preprint arXiv:2004.12226.
Wei, J., Huang, C., Vosoughi, S. and Wei, J., 2020. What Are People Asking About COVID-19? A Question Classification Dataset. arXiv preprint arXiv:2005.12522.
Zhou, C., 2020. Evaluating new evidence in the early dynamics of the novel coronavirus COVID-19 outbreak in Wuhan, China with real time domestic traffic and potential asymptomatic transmissions. medRxiv.
Rahim, M.S.M., Rehman, A., Kurniawan, F., Saba, T.: Ear biometrics for human classification based on region features mining. Biomed. Res. 28(10), 4660–4664 (2017)
Ramzan, F., Khan, M.U.G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., Mehmood, Z.: A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks. J. Med. Syst. 44(2), 37 (2020)
Raza, M., Sharif, M., Yasmin, M., Khan, M.A., Saba, T., Fernandes, S.L.: Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. Futur. Gener. Comput. Syst. 88, 28–39 (2018)
Rehman, A.A., Saba, N., Mahmood, T., Kolivand, T., H.: Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs. Microscopic research and technique 81(7), 737–744 (2018). https://doi.org/10.1002/jemt.23030
Rehman, A.: Light microscopic iris classification using ensemble multi-class support vector machine. Microsc. Res. Tech. 84(5), 982–991 (2021)
Rao, C.S.: Exploration and evaluation of efficient preprocessing and segmentation technique for breast cancer diagnosis based on mammograms. International Journal of Research in Pharmaceutical Sciences 10(3), 2071–2081 (2019)
Saba, T., Rehman, A., Mehmood, Z., Kolivand, H., Sharif, M.: Image enhancement and segmentation techniques for detection of knee joint diseases: A survey. Current Medical Imaging 14(5), 704–715 (2018)
Nasir, M., Khan, M.A., Sharif, M., Javed, M.Y., Saba, T., Ali, H., Tariq, J.: Melanoma detection and classification using computerized analysis of dermoscopic systems: a review. Current Medical Imaging 16(7), 794–822 (2020)
Naz, A., Javed, M.U., Javaid, N., Saba, T., Alhussein, M., Aurangzeb, K.: Short-term electric load and price forecasting using enhanced extreme learning machine optimization in smart grids. Energies 12(5), 1–30 (2019)
Nazir, M., Khan, M. A., Saba, T., & Rehman, A. (2019). Brain Tumor Detection from MRI images using Multi-level Wavelets. In 2019, IEEE International Conference on Computer and Information Sciences (ICCIS) (pp. 1–5).
Nodehi, A., Sulong, G., Al-Rodhaan, M., Al-Dhelaan, A., Rehman, A. and Saba, T., (2014). Intelligent fuzzy approach for fast fractal image compression. EURASIP Journal on Advances in Signal Processing, 2014(1), p.1–9, doi. https://doi.org/10.1186/1687-6180-2014-112.
Norouzi, A., Rahim, M.S.M., Altameem, A., Saba, T., Rad, A.E., Rehman, A., Uddin, M.: Medical image segmentation methods, algorithms, and applications. IETE Tech. Rev. 31(3), 199–213 (2014)
Mughal, B., Muhammad, N., Sharif, M., Rehman, A., Saba, T.: Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC Cancer 18(1), 778 (2018). https://doi.org/10.1186/s12885-018-4638-5
Mughal, B. Muhammad, N. Sharif, M. Saba, T. Rehman, A. (2017) Extraction of breast border and removal of pectoral muscle in wavelet, domain Biomedical Research, vol.28(11), 5041–5043.
Mughal, B. Sharif, M. Muhammad, N. Saba, T. (2018). “A novel classification scheme to decline the mortality rate among women due to breast tumor,” Microscopy Research and Technique, 81(2), 171–180, doi. https://doi.org/10.1002/jemt.22961.
Wang, H., Wei, R., Rao, G., Zhu, J., & Song, B. (2020). Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia. European Radiology, 1.
Saba, T. Rehman, A. Altameem, A. Uddin, M. (2014) Annotated comparisons of proposed preprocessing techniques for script recognition, Neural Computing and Applications Vol. 25(6), pp. 1337–1347, doi. https://doi.org/10.1007/s00521-014-1618-9
Harouni, M., Rahim, M.S.M., Al-Rodhaan, M., Saba, T., Rehman, A., Al-Dhelaan, A.: Online Persian/Arabic script classification without contextual information. The Imaging Science Journal 62(8), 437–448 (2014)
Saba, T., Al-Zahrani, S., Rehman, A.: Expert system for offline clinical guidelines and treatment Life Sci Journal 9(4), 2639–2658 (2012)
Rodriguez-Morales, A. J., Cardona-Ospina, J. A., Gutiérrez-Ocampo, E., Villamizar-Peña, R., et al., (2020). Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel medicine and infectious disease, 101623.
Ebrahimi, M., Saki Malehi, A. and Rahim, F., 2020. COVID-19 Infection in Medical Staffs versus Patients: A Systematic Review and Meta-analysis of Laboratory Findings, Comorbidities, and Clinical Outcome. Fakher, COVID-19 Infection in Medical Staffs versus Patients: A Systematic Review and Meta-analysis of Laboratory Findings, Comorbidities, and Clinical Outcome (April 20, 2020).
Siordia Jr, J. A. (2020). Epidemiology and clinical features of COVID-19: A review of current literature. Journal of Clinical Virology, 104357.
Ahmad, A.M., Sulong, G., Rehman, A., Alkawaz, M.H., Saba, T.: Data hiding based on improved exploiting modification direction method and Huffman coding. J. Intell. Syst. 23(4), 451–459 (2014)
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., ... & Zhao, Y. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
Saba, T., Rehman, A., Al-Dhelaan, A., Al-Rodhaan, M.: Evaluation of current documents image denoising techniques: a comparative study. Appl. Artif. Intell. 28(9), 879–887 (2014)
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., ... & Cheng, Z. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 395(10223), 497-506.
Saba, T.: Automated lung nodule detection and classification based on multiple classifiers voting. Microsc. Res. Tech. 82(9), 1601–1609 (2019)
Liu, T., Hu, J., Kang, M., Lin, L., Zhong, H., Xiao, J., He, G., Song, T., Huang, Q., Rong, Z. and Deng, A., 2020. Transmission dynamics of 2019 novel coronavirus (2019-nCoV).
He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Alyami, J. (2022). Deep Learning-Based Lung Infection Detection Using Radiology Modalities and Comparisons on Benchmark Datasets in COVID-19 Pandemic. In: Saba, T., Rehman, A., Roy, S. (eds) Prognostic Models in Healthcare: AI and Statistical Approaches. Studies in Big Data, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-19-2057-8_18
Download citation
DOI: https://doi.org/10.1007/978-981-19-2057-8_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2056-1
Online ISBN: 978-981-19-2057-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)