RT Journal Article
T1 Tiled Sparse Coding in Eigenspaces for Image Classification.
A1 Arco, Juan E.
A1 Ortiz-García, Andrés
A1 Ramírez, Javier
A1 Zhang, Yu-Dong
A1 Górriz-Sáez, Juan Manuel
K1 Neumonía
K1 Diagnóstico por imagen
K1 Inteligencia artificial
AB 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.
PB World Scientific
YR 2021
FD 2021-12-30
LK https://hdl.handle.net/10630/28108
UL https://hdl.handle.net/10630/28108
LA eng
NO Arco, Juan & Ortiz, Andrés & Ramírez, Javier & Zhang, Yudong & Gorriz, Juan. (2021). Tiled Sparse Coding in Eigenspaces for Image Classification. International Journal of Neural Systems. 32. 10.1142/S0129065722500071.
NO This work was supported by the MCIN/AEI/10.13039/501100011033/ and FEDER “Unamanera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejer´ıa deEconom´ıa, Innovaci´on, Ciencia y Empleo (Juntade Andaluc´ıa) and FEDER under CV20-45250, A TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026