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Tiled Sparse Coding in Eigenspaces for Image Classification

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Identificadores
URI: https://hdl.handle.net/10481/85811
DOI: 10.1142/S0129065722500071
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Autor
Arco Martín, Juan Eloy; Ortiz, Andrés; Ramírez Pérez De Inestrosa, Javier; Zhang, Yu-Dong; Gorriz Sáez, Juan Manuel
Editorial
World Scientific Publishing Company
Materia
COVID-19
 
Computer-aided-diagnosis
 
Deep learning
 
Dictionary
 
Machine learning
 
Medical imaging
 
Pneumonia
 
Sparse coding
 
Fecha
2022-03
Referencia bibliográfica
Published version: Vol. 32, No. 03, 2250007 (2022) [10.1142/S0129065722500071]
Patrocinador
MCIN/ AEI/10.13039/501100011033/; FEDER “Una manera de hacer Europa” under the RTI2018- 098913-B100 project; Consejería de890 Economía, Innovación, Ciencia y Empleo (Junta de Andalucía); FEDER under CV20-45250, A- TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects
Resumen
The 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.
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