2026-06-01T23:34:47Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/281082026-02-03T10:52:44Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Arco, Juan E. author Ortiz-García, Andrés author Ramírez, Javier author Zhang, Yu-Dong author Górriz-Sáez, Juan Manuel author 2021-12-30 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. 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. https://hdl.handle.net/10630/28108 10.1142/S0129065722500071 Neumonía Diagnóstico por imagen Inteligencia artificial Tiled Sparse Coding in Eigenspaces for Image Classification.