Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning

Visuña, Lara; García Blas, Francisco Javier; Yang, Dandi; Carretero Pérez, Jesús

Publication:
Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemas	es
dc.contributor.author	Visuña, Lara
dc.contributor.author	García Blas, Francisco Javier
dc.contributor.author	Yang, Dandi
dc.contributor.author	Carretero Pérez, Jesús
dc.contributor.funder	European Commission	en
dc.date.accessioned	2023-04-24T09:20:01Z
dc.date.available	2023-04-24T09:20:01Z
dc.date.issued	2022-10-15
dc.description.abstract	Background: Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it’s very important to be accurate in the early stages of diagnosis and treatment. Results: We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology’s. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble. Conclusions: To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.	en
dc.description.sponsorship	This work was supported by the Innovative Medicines Initiative 2 Joint Undertaking (JU) under Grant Agreement No. 853989. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Global Alliance for TB Drug Development non-profit organisation, Bill & Melinda Gates Foundation and University of Dundee.	en
dc.format.extent	16
dc.identifier.bibliographicCitation	Visuña, L., Yang, D., Garcia-Blas, J., & Carretero, J. (2022). Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Medical Imaging, 22, 178.	en
dc.identifier.doi	https://doi.org/10.1186/s12880-022-00904-4
dc.identifier.issn	1471-2342
dc.identifier.publicationfirstpage	1
dc.identifier.publicationissue	178
dc.identifier.publicationlastpage	16
dc.identifier.publicationtitle	BMC Medical Imaging	en
dc.identifier.publicationvolume	22
dc.identifier.uri	https://hdl.handle.net/10016/37179
dc.identifier.uxxi	AR/0000032298
dc.language.iso	eng
dc.publisher	BMC
dc.relation.projectID	info:eu-repo/grantAgreement/EC/GA-853989
dc.rights	© The Author(s) 2022.	en
dc.rights	Atribución 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.subject.eciencia	Biología y Biomedicina	es
dc.subject.eciencia	Informática	es
dc.subject.eciencia	Medicina	es
dc.subject.other	CNN	en
dc.subject.other	Covid-19 classification	en
dc.subject.other	Deep ensemble learning	en
dc.subject.other	Grad-CAM	en
dc.subject.other	Stacking	en
dc.subject.other	Voting	en
dc.title	Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication