Tiled Sparse Coding in Eigenspaces for Image Classification

Author:

Arco Juan E12,Ortiz Andrés32,Ramírez Javier12,Zhang Yu-Dong4,Górriz Juan M12

Affiliation:

1. Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain

2. Andalusian Research Institute in Data, Science and Computational Intelligence, Spain

3. Department of Communications Engineering, University of Malaga 29010, Spain

4. School of Informatics, University of Leicester, Leicester LE1 7RH, UK

Abstract

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. blackThese 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 blackfirst 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. blackThen, 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. blackOur 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.

Funder

FEDER

Una manera de hacer Europa

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Networks and Communications,General Medicine

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