AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Overview

Journal Ing Rech Biomed

Specialty Biomedical Engineering

Date 2021 Aug 2

PMID 34336141

Citations 7

Authors

R Karthik

R Menaka

M Hariharan

G S Kathiresan

Affiliations

Soon will be listed here.

Abstract

Background And Objective: In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.

Methods: The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.

Results: The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.

Conclusion: This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

Citing Articles

A new hybrid prediction model of COVID-19 daily new case data.

Li G, Lu J, Chen K, Yang H Eng Appl Artif Intell. 2024; :106692.

PMID: 38620125 PMC: 10291292. DOI: 10.1016/j.engappai.2023.106692.

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.

Malik H, Anees T, Al-Shamaylehs A, Alharthi S, Khalil W, Akhunzada A Diagnostics (Basel). 2023; 13(17).

PMID: 37685310 PMC: 10486427. DOI: 10.3390/diagnostics13172772.

Co-ERA-Net: Co-Supervision and Enhanced Region Attention for Accurate Segmentation in COVID-19 Chest Infection Images.

He Z, Wong A, Yoo J Bioengineering (Basel). 2023; 10(8).

PMID: 37627813 PMC: 10451793. DOI: 10.3390/bioengineering10080928.

A review on lung disease recognition by acoustic signal analysis with deep learning networks.

Sfayyih A, Sulaiman N, Sabry A J Big Data. 2023; 10(1):101.

PMID: 37333945 PMC: 10259357. DOI: 10.1186/s40537-023-00762-z.

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.

Sun W, Feng X, Liu J, Ma H Biomed Signal Process Control. 2022; 79:104099.

PMID: 35996574 PMC: 9385774. DOI: 10.1016/j.bspc.2022.104099.

References

Yang S, Jiang L, Cao Z, Wang L, Cao J, Feng R . Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study. Ann Transl Med. 2020; 8(7):450. PMC: 7210135. DOI: 10.21037/atm.2020.03.132. View

Orsi M, Oliva A, Cellina M . Radiology Department Preparedness for COVID-19: Facing an Unexpected Outbreak of the Disease. Radiology. 2020; 295(3):E8. PMC: 7233380. DOI: 10.1148/radiol.2020201214. View

Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro P . CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection. IEEE Access. 2021; 8:91916-91923. PMC: 8043420. DOI: 10.1109/ACCESS.2020.2994762. View

Hassantabar S, Ahmadi M, Sharifi A . Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos Solitons Fractals. 2020; 140:110170. PMC: 7388764. DOI: 10.1016/j.chaos.2020.110170. View

Xiao L, Li P, Sun F, Zhang Y, Xu C, Zhu H . Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019. Front Bioeng Biotechnol. 2020; 8:898. PMC: 7411489. DOI: 10.3389/fbioe.2020.00898. View

Yoo S, Geng H, Chiu T, Yu S, Cho D, Heo J . Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. Front Med (Lausanne). 2020; 7:427. PMC: 7371960. DOI: 10.3389/fmed.2020.00427. View

Santosh K . AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. J Med Syst. 2020; 44(5):93. PMC: 7087612. DOI: 10.1007/s10916-020-01562-1. View

Perumal V, Narayanan V, Sundar Rajasekar S . Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features. Appl Intell (Dordr). 2022; 51(1):341-358. PMC: 8852781. DOI: 10.1007/s10489-020-01831-z. View

Pereira R, Bertolini D, Teixeira L, Silla Jr C, Costa Y . COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed. 2020; 194:105532. PMC: 7207172. DOI: 10.1016/j.cmpb.2020.105532. View

10.

Bridge J, Meng Y, Zhao Y, Du Y, Zhao M, Sun R . Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models. IEEE J Biomed Health Inform. 2020; 24(10):2776-2786. PMC: 8545159. DOI: 10.1109/JBHI.2020.3012383. View

11.

Khan A, Shah J, Bhat M . CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed. 2020; 196:105581. PMC: 7274128. DOI: 10.1016/j.cmpb.2020.105581. View

12.

Elgendi M, Nasir M, Tang Q, Fletcher R, Howard N, Menon C . The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias. Front Med (Lausanne). 2020; 7:550. PMC: 7461795. DOI: 10.3389/fmed.2020.00550. View

13.

Tsiknakis N, Trivizakis E, Vassalou E, Papadakis G, Spandidos D, Tsatsakis A . Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays. Exp Ther Med. 2020; 20(2):727-735. PMC: 7388253. DOI: 10.3892/etm.2020.8797. View

14.

Toraman S, Alakus T, Turkoglu I . Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals. 2020; 140:110122. PMC: 7357532. DOI: 10.1016/j.chaos.2020.110122. View

15.

Dansana D, Kumar R, Bhattacharjee A, Hemanth D, Gupta D, Khanna A . Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm. Soft comput. 2020; 27(5):2635-2643. PMC: 7453871. DOI: 10.1007/s00500-020-05275-y. View

16.

Imran A, Posokhova I, Qureshi H, Masood U, Riaz M, Ali K . AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked. 2020; 20:100378. PMC: 7318970. DOI: 10.1016/j.imu.2020.100378. View

17.

Abdel-Basset M, Chang V, Mohamed R . HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Appl Soft Comput. 2020; 95:106642. PMC: 7439973. DOI: 10.1016/j.asoc.2020.106642. View

18.

Zebin T, Rezvy S . COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. Appl Intell (Dordr). 2021; 51(2):1010-1021. PMC: 7486976. DOI: 10.1007/s10489-020-01867-1. View

19.

Rubin G, Ryerson C, Haramati L, Sverzellati N, Kanne J, Raoof S . The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology. 2020; 296(1):172-180. PMC: 7233395. DOI: 10.1148/radiol.2020201365. View

20.

Apostolopoulos I, Mpesiana T . Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020; 43(2):635-640. PMC: 7118364. DOI: 10.1007/s13246-020-00865-4. View