Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Typ-2-Diabetes und Adipositas Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende

Die Highlights vom Kongress des American College of Cardiology 2024

Im April diskutierten die Herz-Kreislauf-Spezialisten aus der ganzen Welt auf dem  Kongress des American College of Cardiology (ACC) u.a. neue Therapieansätze bei akutem Myokardinfarkt, koronarer Herzerkrankung, kardiogenem Schock oder Aortenklappenstenose. In unserem Kongress-Dossier haben wir für Sie Berichte über die „Late-Breaking Trials“ und andere wichtige Studien zusammengestellt. 
Erweiterte Suche
Anmelden
nach oben
Journal of Medical Systems
Erschienen in: Journal of Medical Systems 7/2021

04.06.2021 | COVID-19 | Education & Training Zur Zeit gratis

Covid-19 Imaging Tools: How Big Data is Big?

verfasst von: KC Santosh, Sourodip Ghosh

Erschienen in: Journal of Medical Systems | Ausgabe 7/2021

Einloggen, um Zugang zu erhalten

Abstract

In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases.
Literatur
1.
Wu F., Zhao S., Yu B., Chen Y. -M., Wang W., Song Z. -G., Hu Y., Tao Z. -W., Tian J. -H., Pei Y. -Y., et al.: A new coronavirus associated with human respiratory disease in china. Nature 579 (7798): 265–269, 2020PubMedPubMedCentralCrossRef
2.
Cucinotta D., Vanelli M.: Who declares covid-19 a pandemic. Acta Bio Medica: Atenei Parmensis 91 (1): 157, 2020
3.
Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395 (10223): 497–506, 2020PubMedPubMedCentralCrossRef
4.
Vetter P., Vu D.L., L’Huillier A.G., Schibler M., Kaiser L., Jacquerioz F. (2020) Clinical features of covid-19. British Medical Journal Publishing Group
5.
Ai T., Yang Z., Hou H., Zhan C., Chen C., Lv W., Tao Q., Sun Z., Xia L.: Correlation of chest ct and rt-pcr testing in coronavirus disease 2019 (covid-19) in China: A report of 1014 cases. Radiology 296: 200642, 2020CrossRef
6.
Shi F., Wang J., Shi J., Wu Z., Wang Q., Tang Z., He K., Shi Y., Shen D.: Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev. Biomed. Eng. 14: 4–15, 2020CrossRef
7.
McCall B.: Covid-19 and artificial intelligence: Protecting health-care workers and curbing the spread. Lancet Digit. Health 2 (4): e166–e167, 2020PubMedPubMedCentralCrossRef
8.
Vaishya R., Javaid M., Khan I.H., Haleem A.: Artificial intelligence (ai) applications for covid-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev. 14 (4): 337–339, 2020CrossRef
9.
Albahri A., Hamid R.A., et al.: Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (covid-19): A systematic review. J. Med. Syst. 7: 44, 2020
10.
Mei X., Lee H. -C., Diao K. -y., Huang M., Lin B., Liu C., Xie Z., Ma Y., Robson P.M., Chung M., et al.: Artificial intelligence–enabled rapid diagnosis of patients with covid-19. Nat. Med. 26: 1–5, 2020CrossRef
11.
Huang L., Han R., Ai T., Yu P., Kang H., Tao Q., Xia L.: Serial quantitative chest ct assessment of covid-19: Deep-learning approach. Radiol.: Cardiothorac. Imaging 2 (2): e200075, 2020
12.
Oh Y., Park S., Ye J.C.: Deep learning covid-19 features on cxr using limited training data sets. IEEE Trans. Med. Imaging 39: 2626–2637, 2020CrossRef
13.
Fan D. -P., Zhou T., Ji G. -P., Zhou Y., Chen G., Fu H., Shen J., Shao L.: Inf-net: Automatic covid-19 lung infection segmentation from ct images. IEEE Trans. Med. Imaging 39: 2626–2637, 2020PubMedCrossRef
14.
Elasnaoui K., Chawki Y. (2020) Using x-ray images and deep learning for automated detection of coronavirus disease. J. Biomolecular Struct. Dyn., no. just-accepted, 1–22
15.
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. 44 (5): 1–5, 2020CrossRef
16.
Santosh K.: Covid-19 prediction models and unexploited data. J. Med. Syst. 44 (9): 1–4, 2020CrossRef
17.
Bhapkar H., Mahalle P.N., Dey N., Santosh K.: Revisited covid-19 mortality and recovery rates: Are we missing recovery time period? J. Med. Syst. 44 (12): 1–5, 2020CrossRef
18.
Kang M., Gurbani S.S., Kempker J.A.: The published scientific literature on covid-19: An analysis of pubmed abstracts. J. Med. Syst. 45 (1): 1–2, 2021CrossRef
19.
Farid A.A., Selim G.I., Awad H., Khater A.: A novel approach of ct images feature analysis and prediction to screen for corona virus disease (covid-19). Int. J. Sci. Eng. Res. 11 (3): 1–9, 2020
20.
Singh D., Kumar V., Kaur M.: Classification of covid-19 patients from chest ct images using multi-objective differential evolution–based convolutional neural networks. Eur. J. Clin. Microbiol. Infect. Dis. 39: 1–11, 2020CrossRef
21.
Hasan A.M., AL-Jawad M.M., Jalab H.A., Shaiba H., Ibrahim R.W., AL-Shamasneh A.R.: Classification of covid-19 coronavirus, pneumonia and healthy lungs in ct scans using q-deformed entropy and deep learning features. Entropy 22 (5): 517, 2020PubMedCentralCrossRef
22.
Mukherjee H., Ghosh S., Dhar A., Obaidullah S.M., Santosh K., Roy K.: Deep neural network to detect covid-19: One architecture for both ct scans and chest x-rays. Appl. Intell. 51: 1–13, 2020
23.
Xu X., Jiang X., Ma C., Du P., Li X., Lv S., Yu L., Ni Q., Chen Y., Su J., et al.: A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6: 1122–1129, 2020PubMedCrossRef
24.
Loey M., Manogaran G., Khalifa N.E.M. (2020) A deep transfer learning model with classical data augmentation and cgan to detect covid-19 from chest ct radiography digital images. Neural Comput. Appl. 1–13
25.
Wu X., Hui H., Niu M., Li L., Wang L., He B., Yang X., Li L., Li H., Tian J., et al.: Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. European Journal of Radiology 128: 109041, 2020PubMedPubMedCentralCrossRef
26.
Pathak Y., Shukla P.K., Tiwari A., Stalin S., Singh S., Shukla P.K. (2020) Deep transfer learning based classification model for covid-19 disease. IRBM
27.
Amyar A., Modzelewski R., Li H., Ruan S.: Multi-task deep learning based ct imaging analysis for covid-19 pneumonia: Classification and segmentation. Comput. Biol. Med. 126: 104037, 2020PubMedPubMedCentralCrossRef
28.
Li L., Qin L., Xu Z., Yin Y., Wang X., Kong B., Bai J., Lu Y., Fang Z., Song Q., et al. (2020) Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology 200905
29.
Ardakani A.A., Kanafi A.R., Acharya U.R., Khadem N., Mohammadi A.: Application of deep learning technique to manage covid-19 in routine clinical practice using ct images: Results of 10 convolutional neural networks. Comput. Biol. Med. 121: 103795, 2020PubMedPubMedCentralCrossRef
30.
Ko H., Chung H., Kang W.S., Kim K.W., Shin Y., Kang S.J., Lee J.H., Kim Y.J., Kim N.Y., Jung H., et al.: Covid-19 pneumonia diagnosis using a simple 2d deep learning framework with a single chest ct image: Model development and validation. J. Med. Internet Res. 22 (6): e19569, 2020PubMedPubMedCentralCrossRef
31.
Alshazly H., Linse C., Barth E., Martinetz T.: Explainable covid-19 detection using chest ct scans and deep learning. Sensors 21 (2): 455, 2021PubMedCentralCrossRef
32.
Ni Q., Sun Z.Y., Qi L., Chen W., Yang Y., Wang L., Zhang X., Yang L., Fang Y., Xing Z., et al.: A deep learning approach to characterize 2019 coronavirus disease (covid-19) pneumonia in chest ct images. Eur. Radiol. 30 (12): 6517–6527 , 2020PubMedCrossRef
33.
Zhou T., Lu H., Yang Z., Qiu S., Huo B., Dong Y.: The ensemble deep learning model for novel covid-19 on ct images. Appl. Soft Comput. 98: 106885, 2021PubMedCrossRef
34.
Chen J., Wu L., Zhang J., Zhang L., Gong D., Zhao Y., Chen Q., Huang S., Yang M., Yang X., et al.: Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci. Rep. 10 (1): 1–11, 2020
35.
Alqudah A.M., Qazan S., Alquran H., Qasmieh I.A., Alqudah A.: Covid-2019 detection using x-ray images and artificial intelligence hybrid systems.. In: Biomedical Signal and Image Analysis and Project; Biomedical Signal and Image Analysis and Machine Learning Lab. Boca Raton, 2019
36.
Ucar F., Korkmaz D.: Covidiagnosis-net: Deep bayes-squeezenet based diagnostic of the coronavirus disease 2019 (covid-19) from x-ray images. Med. Hypotheses 140: 109761, 2020PubMedPubMedCentralCrossRef
37.
Loey M., Smarandache F., Khalifa N.E.M.: Within the lack of chest covid-19 x-ray dataset: A novel detection model based on gan and deep transfer learning. Symmetry 12 (4): 651, 2020CrossRef
38.
Ozturk T., Talo M., Yildirim E.A., Baloglu U.B., Yildirim O., Acharya U.R.: Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput. Biol. Med 121: 103792, 2020PubMedPubMedCentralCrossRef
39.
Mukherjee H., Ghosh S., Dhar A., Obaidullah S., Santosh K., Roy K. (2021) Shallow convolutional neural network for covid-19 outbreak screening using chest x-rays. Cognit. Comput.
40.
41.
Civit-Masot J., Luna-Perejón F., Domínguez Morales M., Civit A.: Deep learning system for covid-19 diagnosis aid using x-ray pulmonary images. Appl. Sci. 10 (13): 4640, 2020CrossRef
42.
Rahimzadeh M., Attar A. (2020) A modified deep convolutional neural network for detecting covid-19 and pneumonia from chest x-ray images based on the concatenation of xception and resnet50v2. Inform. Med. Unlocked 100360
43.
Ismael A.M., Şengür A.: Deep learning approaches for covid-19 detection based on chest x-ray images. Expert Syst. Appl. 164: 114054, 2021PubMedCrossRef
44.
Vaid S., Kalantar R., Bhandari M.: Deep learning covid-19 detection bias: Accuracy through artificial intelligence. Int. Orthop 138: 1, 2020
45.
Panwar H., Gupta P., Siddiqui M.K., Morales-Menendez R., Singh V.: Application of deep learning for fast detection of covid-19 in x-rays using ncovnet. Chaos Solitons Fractals 97: 109944, 2020CrossRef
46.
Nour M., Cömert Z., Polat K.: A novel medical diagnosis model for covid-19 infection detection based on deep features and bayesian optimization. Appl. Soft Comput. 97: 106580, 2020PubMedPubMedCentralCrossRef
47.
Apostolopoulos I.D., Mpesiana T.A.: Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 140: 1, 2020
48.
Toraman S., Alakus T.B., 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 140: 110122, 2020PubMedPubMedCentralCrossRef
49.
Brunese L/, Mercaldo F., Reginelli A., Santone A.: Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-rays. Comput. Methods Progr. Biomed. 196: 105608, 2020CrossRef
50.
Jain G., Mittal D., Thakur D., Mittal M.K.: A deep learning approach to detect covid-19 coronavirus with x-ray images. Biocybern. Biomed. Eng. 40 (4): 1391–1405, 2020PubMedPubMedCentralCrossRef
51.
Khan A.I., Shah J.L., Bhat M.M.: Coronet: A deep neural network for detection and diagnosis of covid-19 from chest x-ray images. Comput. Methods Progr. Biomed. 10: 105581, 2020CrossRef
52.
Sitaula C., Hossain M.B.: Attention-based vgg-16 model for covid-19 chest x-ray image classification. Appl. Intell. 96: 1–14, 2020
53.
Sitaula C., Aryal S.: New bag of deep visual words based features to classify chest x-ray images for covid-19 diagnosis. Health Inf. Sci. Syst. 43: 915–925, 2020
54.
Wang L., Lin Z.Q., Wong A.: Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci. Rep. 10 (1): 1–12, 2020
55.
Ismael A.M., Şengür A.: The investigation of multiresolution approaches for chest x-ray image based covid-19 detection. Health Inf. Sci. Syst. 8 (1): 1–11, 2020CrossRef
56.
Marques G., Agarwal D., de la Torre Díez I.: Automated medical diagnosis of covid-19 through efficientnet convolutional neural network. Appl. Soft Comput. 96: 106691, 2020PubMedPubMedCentralCrossRef
57.
Das D., Santosh K., Pal U.: Truncated inception net: Covid-19 outbreak screening using chest x-rays. Phys. Eng. Sci. Med. 43 (3): 915–925, 2020PubMedPubMedCentralCrossRef
Metadaten
Titel
Covid-19 Imaging Tools: How Big Data is Big?
verfasst von
KC Santosh
Sourodip Ghosh
Publikationsdatum
04.06.2021
Verlag
Springer US
Schlagwort
COVID-19
Erschienen in
Journal of Medical Systems / Ausgabe 7/2021
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-021-01747-2

Weitere Artikel der Ausgabe 7/2021

Journal of Medical Systems 7/2021 Zur Ausgabe

Education & Training

Lessons Learned from the Usability Evaluation of a Simulated Patient Dialogue System

EFMI STC 2019 on ICT for Health Science Research

A Topical Collection on ICT for Health Science Research – EFMI Special Topic Conference

Image & Signal Processing

Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19

Mobile & Wireless Health

Quality Assessment of Health-apps using a Public Agency Quality and Safety Seal. The Appsaludable Case

Patient Facing Systems

Assessing Patient Proficiency with Internet-Connected Technology and Their Preferences for E-Health in Cirrhosis

Patient Facing Systems

Association between Patient Portal use and Office Visits