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dc.contributor.authorArco Martín, Juan Eloy 
dc.contributor.authorOrtiz, Andrés
dc.contributor.authorRamírez Pérez De Inestrosa, Javier 
dc.contributor.authorZhang, Yu-Dong
dc.contributor.authorGorriz Sáez, Juan Manuel 
dc.date.accessioned2023-11-21T12:11:54Z
dc.date.available2023-11-21T12:11:54Z
dc.date.issued2022-03
dc.identifier.citationPublished version: Vol. 32, No. 03, 2250007 (2022) [10.1142/S0129065722500071]es_ES
dc.identifier.urihttps://hdl.handle.net/10481/85811
dc.description.abstractThe automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.es_ES
dc.description.sponsorshipMCIN/ AEI/10.13039/501100011033/es_ES
dc.description.sponsorshipFEDER “Una manera de hacer Europa” under the RTI2018- 098913-B100 projectes_ES
dc.description.sponsorshipConsejería de890 Economía, Innovación, Ciencia y Empleo (Junta de Andalucía)es_ES
dc.description.sponsorshipFEDER under CV20-45250, A- TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projectses_ES
dc.language.isoenges_ES
dc.publisherWorld Scientific Publishing Companyes_ES
dc.subjectCOVID-19es_ES
dc.subjectComputer-aided-diagnosises_ES
dc.subjectDeep learninges_ES
dc.subjectDictionaryes_ES
dc.subjectMachine learninges_ES
dc.subjectMedical imaginges_ES
dc.subjectPneumonia es_ES
dc.subjectSparse codinges_ES
dc.titleTiled Sparse Coding in Eigenspaces for Image Classificationes_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1142/S0129065722500071
dc.type.hasVersionAMes_ES


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