Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Multimedia Information Retrieval
Erschienen in: International Journal of Multimedia Information Retrieval 1/2021

20.02.2021 | Regular Paper

Design ensemble deep learning model for pneumonia disease classification

verfasst von: Khalid El Asnaoui

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 1/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).
Vorheriger Artikel Music similarity measurement and recommendation system using convolutional neural networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Orbann C, Sattenspiel L, Miller E, Dimka J (2017) Defining epidemics in computer simulation models: how do definitions influence conclusions? Epidemics 19:24–32CrossRef
2.
Elasnaoui K, Chawki Y, Radeva P, Idri A (2020) Automated methods for detection and classification pneumonia based on X-ray images using deep learning. arXiv preprint arXiv:​2003.​14363
3.
4.
Zerouaoui H, Idri A, El Asnaoui K (2020) Machine learning and image processing for breast cancer: a systematic map. In: World conference on information systems and technologies. Springer, Cham, pp 44–53
5.
Ouhda M, El Asnaoui K, Ouanan M, Aksasse B (2017) Content-based image retrieval using convolutional neural networks. In: First international conference on real time intelligent systems. Springer, Cham, pp 463–476
6.
Janghel RR, Shukla A, Sharma S, Gnaneswar AV (2014) Evolutionary ensemble model for breast cancer classification. In: International conference in swarm intelligence. Springer, Cham, pp 8–16
7.
Kwon H, Park J, Lee Y (2019) Stacking ensemble technique for classifying breast cancer. Healthc Inf Res 25(4):283–288CrossRef
8.
Zilly J, Buhmann JM, Mahapatra D (2017) Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph 55:28–41CrossRef
9.
10.
11.
Taghanaki SA, Das A, Hamarneh G (2018) Vulnerability analysis of chest X-ray image classification against adversarial attacks. In: Understanding and interpreting machine learning in medical image computing applications. Springer, Cham, pp 87–94
12.
Ghaderzadeh M, Asadi F (2020) Deep learning in detection and diagnosis of covid-19 using radiology modalities: a systematic review. arXiv preprint arXiv:​2012.​11577
13.
Guendel S, Grbic S, Georgescu B, Liu S, Maier A, Comaniciu D (2018) Learning to recognize abnormalities in chest x-rays with location-aware dense networks. In: Iberoamerican congress on pattern recognition. Springer, Cham, pp 757–765
14.
Khan W, Zaki N, Ali L (2020) Intelligent pneumonia identification from chest X-rays: a systematic literature review. medRxiv
15.
Barrientos R, Roman-Gonzalez A, Barrientos F, Solis L, Correa M et al (2016) Automatic detection of pneumonia analyzing ultrasound digital images. In: 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI). IEEE, pp 1–4
16.
Ahmad WSHMW, Zaki WMDW, Fauzi MFA, Tan WH (2016) Classification of infection and fluid regions in chest X-ray images. In: 2016 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp 1–5
17.
Khobragade S, Tiwari A, Patil CY, Narke V (2016) Automatic detection of major lung diseases using chest radiographs and classification by feed-forward artificial neural network. In: 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES). IEEE, pp 1–5
18.
Cicero M, Bilbily A, Colak E, Dowdell T, Gray B, Perampaladas K, Barfett J (2017) Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest Radiol 52(5):281–287CrossRef
19.
Dong Y, Pan Y, Zhang J, Xu W (2017) Learning to read chest X-ray images from 16000 + examples using CNN. In: 2017 IEEE/ACM international conference on connected health: applications, systems and engineering technologies (CHASE). IEEE, pp 51–57
20.
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T et al (2017) Chexnet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:​1711.​05225
21.
Islam MT, Aowal MA, Minhaz AT, Ashraf K (2017) Abnormality detection and localization in chest X-rays using deep convolutional neural networks. arXiv preprint arXiv:​1705.​09850
22.
Madani A, Moradi M, Karargyris A, Syeda-Mahmood T (2018) Chest X-ray generation and data augmentation for cardiovascular abnormality classification. In: Medical imaging 2018: image processing, vol 10574. International Society for Optics and Photonics, p 105741 M
23.
Rajaraman S, Candemir S, Kim I, Thoma G, Antani S (2018) Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci 8(10):1715CrossRef
24.
Ausawalaithong W, Thirach A, Marukatat S, Wilaiprasitporn T (2018) Automatic lung cancer prediction from chest X-ray images using the deep learning approach. In: 2018 11th biomedical engineering international conference (BMEiCON). IEEE, pp 1–5
25.
Correa M, Zimic M, Barrientos F, Barrientos R, Román-Gonzalez A, Pajuelo MJ et al (2018) Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition. PLoS ONE 13(12):e0206410CrossRef
26.
Gu X, Pan L, Liang H, Yang R (2018) Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography. In: Proceedings of the 3rd international conference on multimedia and image processing, pp 88–93
27.
Ke Q, Zhang J, Wei W, Połap D, Woźniak M, Kośmider L, Damaševĭcius R (2019) A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert Syst Appl 126:218–232CrossRef
28.
Saraiva AA, Ferreira NMF, de Sousa LL, Costa NJC, Sousa JVM, Santos DBS et al (2019) Classification of images of childhood pneumonia using convolutional neural networks. In: BIOIMAGING, pp 112–119
29.
Varshni D, Thakral K, Agarwal L, Nijhawan R, Mittal A (2019) Pneumonia detection using CNN based feature extraction. In: 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT). IEEE, pp 1–7
30.
Siddiqi R (2019) Automated pneumonia diagnosis using a customized sequential convolutional neural network. In: Proceedings of the 2019 3rd international conference on deep learning technologies, pp 64–70
31.
Ayan E, Ünver HM (2019) Diagnosis of pneumonia from chest X-ray images using deep learning. In: 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT). IEEE, pp 1–5
32.
Sirazitdinov I, Kholiavchenko M, Mustafaev T, Yixuan Y, Kuleev R, Ibragimov B (2019) Deep neural network ensemble for pneumonia localization from a large-scale chest X-ray database. Comput Electr Eng 78:388–399CrossRef
33.
Liang G, Zheng L (2020) A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput Methods Programs Biomed 187:104964CrossRef
34.
Bozickovic J, Lazic I, Turukalo TL (2020) Pneumonia detection and classification from X-ray images—a deep learning approach
35.
Abbas A, Abdelsamea MM, Gaber MM (2020) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv preprint arXiv:​2003.​13815
36.
Wang L, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv preprint arXiv:​2003.​09871
37.
Sethy PK, Behera SK (2020) Detection of coronavirus disease (covid-19) based on deep features
38.
Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43:635–640CrossRef
39.
40.
Narin A, Kaya C, Pamuk Z (2020) Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:​2003.​10849
41.
Hemdan EED, Shouman MA, Karar ME (2020) Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:​2003.​11055
42.
Zhang J, Xie Y, Li Y, Shen C, Xia Y (2020) Covid-19 screening on chest X-ray images using deep learning based anomaly detection. arXiv preprint arXiv:​2003.​12338
43.
Farooq M, Hafeez A (2020) Covid-resnet: a deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:​2003.​14395
44.
Maghdid HS, Asaad AT, Ghafoor KZ, Sadiq AS, Khan MK (2020) Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. arXiv preprint arXiv:​2004.​00038
45.
Apostolopoulos I, Aznaouridis S, Tzani M (2020) Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. arXiv preprint arXiv:​2004.​00338
46.
Habib N, Hasan MM, Reza MM, Rahman MM (2020) Ensemble of CheXNet and VGG-19 feature extractor with random forest classifier for pediatric pneumonia detection. SN Comput Sci 1(6):1–9CrossRef
47.
Chouhan V, Singh SK, Khamparia A, Gupta D, Tiwari P, Moreira C et al (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci 10(2):559CrossRef
48.
El Asnaoui K, Chawki Y, Aksasse B, Ouanan M (2015) A new color descriptor for content-based image retrieval: application to coil-100. J Digit Inf Manag 13(6):473
49.
El Asnaoui K, Chawki Y, Aksasse B, Ouanan M (2016) Efficient use of texture and color features in content-based image retrieval (CBIR). Int J Appl Math Stat 54(2):54–65MathSciNetMATH
50.
Chawki Y, El Asnaoui K, Ouanan M, Aksasse B (2018) Content frequency and shape features based on CBIR: application to color images. Int J Dyn Syst Differ Eqn 8(1–2):123–135MATH
51.
Bhandary A, Prabhu GA, Rajinikanth V, Thanaraj KP, Satapathy SC, Robbins DE, Shasky C, Zhang YD, Tavares JMRS, Raja NSM (2020) Deep-learning framework to detect lung abnormality—a study with chest X-ray and lung CT scan images. Pattern Recogn Lett 129:271–278CrossRef
52.
Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning, rXiv:1602.07261
53.
He K, Zhang X, Ren S, Sunet J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition CVPR’2016, pp 770–778
54.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proceeding of the IEEE conference on computer vision and pattern recognition (CVPR), Salt Lake City, UT, pp 4510–4520
55.
Braga PL, Oliveira AL, Ribeiro GH, Meira SR (2007) Bagging predictors for estimation of software project effort. In: 2007 international joint conference on neural networks. IEEE, pp 1595–1600
56.
Azzeh M, Nassif AB, Minku LL (2015) An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation. J Syst Softw 103:36–52CrossRef
57.
Ouhda M, El Asnaoui K, Ouanan M, Aksasse B (2017) Using image segmentation in content-based image retrieval method. In: International conference on advanced information technology, services and systems. Springer, Cham, pp 179–195
Metadaten
Titel
Design ensemble deep learning model for pneumonia disease classification
verfasst von
Khalid El Asnaoui
Publikationsdatum
20.02.2021
Verlag
Springer London
Erschienen in
International Journal of Multimedia Information Retrieval / Ausgabe 1/2021
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-021-00204-7

Weitere Artikel der Ausgabe 1/2021

International Journal of Multimedia Information Retrieval 1/2021 Zur Ausgabe

Regular Paper

An automatic approach of audio feature engineering for the extraction, analysis and selection of descriptors

Regular Paper

Music similarity measurement and recommendation system using convolutional neural networks

Regular Paper

Cluster-based quotas for fairness improvements in music recommendation systems

Trends and Surveys

Generative adversarial networks: a survey on applications and challenges