Skip to content
2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background

Artificial intelligence (AI) is rapidly evolving in healthcare, with transformative potential. AI revolutionizes medical imaging by enabling online self-diagnosis for patients and improving diagnostic accuracy for healthcare professionals. While valuable datasets aid machine learning in disease detection, challenges persist in diagnosing similar lung conditions from chest X-rays. Integrating AI into healthcare holds promise for enhanced outcomes and efficiency.

Objective

In this article, we aim to present a new AI model that solves this challenge by allowing the differentiation, diagnosis and classification of three distinct diseases, whose symptoms are very similar. The fundamental contribution is to reduce the number of parameters used while maintaining the same level of precision for use in embedded systems.

Methods

Our proposed model combines the power of the neural network using the SqueezeNet architecture with a set of machine learning algorithms as classifiers, including logistic regression, support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and naive Bayes. The chest X-ray dataset used in the proposed model consists of CXR images that are classified into four categories: pneumonia, tuberculosis, COVID-19, and normal cases.

Results

Our proposed model demonstrated remarkable accuracy (97,32%), precision (97,33), F1 score (97,31%), recall (97,30%), and AUC (99,40), which is close to the best model. Whereas, the number of parameters used by our model (4,6 M) is very small compared to the best model in the literature (47M).

Conclusion

The model demonstrated good classification accuracy. In addition, the proposed model has the ability to use fewer parameters, which means it requires less internal memory and computing resources.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056258742230920062315
2024-01-01
2025-08-19

  • From This Site

    /content/journals/cmir/10.2174/0115734056258742230920062315
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. PianykhO.S. GuitronS. ParkeD. ZhangC. PandharipandeP. BrinkJ. RosenthalD. Improving healthcare operations management with machine learning.Nat. Mach. Intell.20202526627310.1038/s42256‑020‑0176‑3
     [Google Scholar]
  2. AbardaA. DakkonM. AzhariM. ZaaloulA. KhabouzeM. Latent transition analysis (LTA): A method for identifying differences in longitudinal change among unobserved groups.Procedia Comput. Sci.20201701116112110.1016/j.procs.2020.03.059
     [Google Scholar]
  3. CengilE. CinarA. A deep learning based approach to lung cancer identification.2018 International Conference on Artificial Intelligence and Data Processing (IDAP)Malatya, TurkeyIEEE1510.1109/IDAP.2018.8620723
     [Google Scholar]
  4. AbardaA. DakkonM. AsmiK. BentalebY. Evaluating information criteria in latent class analysis: Application to identify classes of breast cancer dataset.Int J Data AnalTech Strat2021131/2728710.1504/IJDATS.2021.114669
     [Google Scholar]
  5. DashS. ShakyawarS.K. SharmaM. KaushikS. Big data in healthcare: Management, analysis and future prospects.J. Big Data2019615410.1186/s40537‑019‑0217‑0
     [Google Scholar]
  6. TsivgoulisM. PapastergiouT. MegalooikonomouV. An improved SqueezeNet model for the diagnosis of lung cancer in CT scans.Machine Learning with Applications20221010039910.1016/j.mlwa.2022.100399
     [Google Scholar]
  7. MadhaviK.R. MadhaviG. KrishnaveniC.V. KoraP. COVID-19 Detection Using Deep Learning.20th International Conference on Hybrid Intelligent Systems (HIS 2020)December 14-16, 2020263269
     [Google Scholar]
  8. CocciaM. Sources of technological innovation: Radical and incremental innovation problem-driven to support competitive advantage of firms.Technol. Anal. Strateg. Manage.20172991048106110.1080/09537325.2016.1268682
     [Google Scholar]
  9. AsifS. WenhuiY. JinhaiS. Detection of COVID‐19 from chest X‐ray images: Boosting the performance with convolutional neural network and transfer learning.Expert Syst.2023401e13099
     [Google Scholar]
  10. AsifS. WenhuiY. JinhaiS. CVD19-Net: An Automated Deep Learning Model for COVID-19 Screening using Chest CT Images.2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)Las Vegas, NV, USAIEEE202220492058
     [Google Scholar]
  11. AsifS. ZhaoM. TangF. ZhuY. A deep learning-based framework for detecting COVID-19 patients using chest X-rays.Multimedia Syst.20222841495151310.1007/s00530‑022‑00917‑735341212
     [Google Scholar]
  12. KhanS.U. IslamN. JanZ. Ud DinI. RodriguesJ.J.P.C. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning.Pattern Recognit. Lett.20191251610.1016/j.patrec.2019.03.022
     [Google Scholar]
  13. AduK. WalkerJ. MensahP.K. SqueezeCapsNet: Enhancing capsule networks with squeezenet for holistic medical and complex images. Multimedia Tools and Applications.Multimedia Tools and Applications20232023
     [Google Scholar]
  14. NeubertJ. HaqueI.R.I Biomedical image segmentation by deep learning methods.Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and ApplicationsWileyHoboken2021131154
     [Google Scholar]
  15. MaitiA. AbardaA. HaniniM A New Hybrid Artificial Intelligence Model for Diseases Identification.The Proceedings of the International Conference on Smart City Applications.SpringerCham2022825836
     [Google Scholar]
  16. El GannourO. HamidaS. CherradiB. Al-SaremM. RaihaniA. SaeedF. HadwanM. Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique.Electronics (Basel)202111110310.3390/electronics11010103
     [Google Scholar]
  17. MamalakisM. SwiftA.J. VorselaarsB. RayS. WeeksS. DingW. ClaytonR.H. MackenzieL.S. BanerjeeA. DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.Comput. Med. Imaging Graph.20219410200810.1016/j.compmedimag.2021.10200834763146
     [Google Scholar]
  18. IqbalM.J. JavedZ. SadiaH. QureshiI.A. IrshadA. AhmedR. MalikK. RazaS. AbbasA. PezzaniR. Sharifi-RadJ. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: Looking into the future.Cancer Cell Int.202121127010.1186/s12935‑021‑01981‑134020642
     [Google Scholar]
  19. JaiswalA.K. TiwariP. KumarS. GuptaD. KhannaA. RodriguesJ.J.P.C. Identifying pneumonia in chest X-rays: A deep learning approach.Measurement201914551151810.1016/j.measurement.2019.05.076
     [Google Scholar]
  20. JaiswalA.K. TiwariP. KumarS. GuptaD. KhannaA. RodriguesJ.J.P.C. A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images.Biomed Sig Proc Cont202271B103182
     [Google Scholar]
  21. GhoshP. AzamS. KarimA. JonkmanM. HasanM.Z. Use of efficient machine learning techniques in the identification of patients with heart diseases.2021 the 5th International Conference on Information System and Data Mining2021142010.1145/3471287.3471297
     [Google Scholar]
  22. KaplanA. CaoH. FitzGeraldJ.M. IannottiN. YangE. KocksJ.W.H. KostikasK. PriceD. ReddelH.K. TsiligianniI. VogelmeierC.F. PfisterP. MastoridisP. Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis.J. Allergy Clin. Immunol. Pract.2021962255226110.1016/j.jaip.2021.02.01433618053
     [Google Scholar]
  23. TobíasL. DucournauA. RousseauF. Convolutional Neural Networks for object recognition on mobile devices: A case study.2016 23rd International Conference on Pattern Recognition (ICPR).Cancun, MexicoIEEE2016
     [Google Scholar]
  24. IandolaF.N. HanS. MoskewiczM.W. AshrafK. DallyW.J. KeutzerK. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size.arxiv:1602.073602016
     [Google Scholar]
  25. LeeH. UllahI. WanW. GaoY. FangZ. Real-time vehicle make and model recognition with the residual SqueezeNet architecture.Sensors (Basel)201919598210.3390/s1905098230813512
     [Google Scholar]
  26. LuY. Yan-yanS. Decision tree methods: Applications for classification and predictionShanghai Arch Psychiatry.2015272130135
     [Google Scholar]
  27. VellidoA. Martín-GuerreroJ.D. LisboaP.J. A survey of fuzzy decision trees and their applications.Int. J. Approx. Reason.2012534749774
     [Google Scholar]
  28. CervantesJ. García-LamontF. RodríguezL. Lopez-ChauA. A comprehensive survey on support vector machine classification: Applications, challenges and trends.Neurocomputing20204087
     [Google Scholar]
  29. PushpaS. NarendraS. KantK. Diagnosing of disease using machine learning.Machine learning and the internet of medical things in healthcare.Academic PressMassachusetts20218911110.1016/B978‑0‑12‑821229‑5.00003‑3
     [Google Scholar]
  30. AndersonJ. SmithR. Logistic regression in health research: Applications and challenges.J. Health Sci.2022183234249
     [Google Scholar]
  31. SinghA. DuttaS. SharmaA. Machine learning in healthcare: A review.Proceedings of the International Conference on Computational Intelligence and Data Science.IEEE2019236241
     [Google Scholar]
  32. AbhilashP. M. ChakradharD. Sustainability improvement of WEDM process by analysing and classifying wire rupture using kernel-based naive Bayes classifier.J. Braz. Soc. Mech. Sci. Eng.20214319
     [Google Scholar]
  33. PushpaS. NarendraS. KantK. Diagnosing of disease using machine learning.Machine learning and the internet of medical things in healthcare.Academic PressMassachusetts20218911110.1016/B978‑0‑12‑821229‑5.00003‑3
     [Google Scholar]
  34. AciM. İnanC. AvciM. A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm.Expert Syst. Appl.20103775061506710.1016/j.eswa.2009.12.004
     [Google Scholar]
  35. SobkowiczP. KascheskyM. BouchardG. Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web.Gov. Inf. Q.201229447047910.1016/j.giq.2012.06.005
     [Google Scholar]
  36. https://www.kaggle.com/datasets/jtiptj/chest-xray-pneumoniacovid19tuberculosis
  37. UcarF. KorkmazD. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images.Med. Hypotheses202014010976110.1016/j.mehy.2020.10976132344309
     [Google Scholar]
  38. JungA. Imgaug documentation.2019Available From: https://imgaug.readthedocs.io/en/latest/
  39. KimS. RimB. ChoiS. LeeA. MinS. HongM. Deep learning in multi-class lung diseases’ classification on chest X-ray images.Diagnostics (Basel)202212491510.3390/diagnostics1204091535453963
     [Google Scholar]
  40. YaselianiM. HamadaniA.Z. MaghsoodiA.I. MosaviA. Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers.IEEE Access202210621106212810.1109/ACCESS.2022.3182498
     [Google Scholar]
  41. HemdanE.E-D. ShoumanM.A. KararM.E Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images.arXiv:2003.110552020
     [Google Scholar]
  42. MarquesG. AgarwalD. de la Torre DíezI. Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network.Appl. Soft Comput.20209610669110.1016/j.asoc.2020.106691
     [Google Scholar]
  43. SanidaT. SiderisA. TsiktsirisD. DasygenisM. Lightweight neural network for COVID-19 detection from chest X-ray images implemented on an embedded system.Technologies (Basel)20221023710.3390/technologies10020037
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056258742230920062315
Loading
An Optimal Model Combining SqueezeNet and Machine Learning Methods for Lung Disease Diagnosis
Curr. Med. Imaging 20, e15734056258742 (2024); https://doi.org/10.2174/0115734056258742230920062315
/content/journals/cmir/10.2174/0115734056258742230920062315
/content/journals/cmir/10.2174/0115734056258742230920062315
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Decision tree; Deep learning; KNN; Logistic regression; Lung diseases diagnosis; SqueezeNet; SVM

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test