RT Journal Article
T1 Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification.
A1 Arco, Juan E.
A1 Ortiz-García, Andrés
A1 Ramírez, Javier
A1 Martínez-Murcia, Francisco Jesús
A1 Zhang, Yu-Dong
A1 Broncano, Jordi
A1 Berbís, Álvaro
A1 Royuela-del-Val, Javier
A1 Luna, Antonio
A1 Górriz-Sáez, Juan Manuel
K1 Diagnóstico por imagen
K1 Diagnóstico - Proceso de datos
K1 Medicina - Proceso de datos
AB The emergence of new technologies has changed the way clinicians perform diagnosis. Medical imaging play a crucial role in this process, given the amount of information that they usually provide as non-invasive techniques. Despite the high quality offered by these images and the expertise of clinicians, the diagnostic process is not a straightforward task since different pathologies can have similar signs and symptoms. For this reason, it is extremely useful to assist this process with the inclusion of an automatic tool that reduces the bias when analyzing this kind of images. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify relevant patterns while providing information about the reliability of the classification. Specifically, each image is divided into patches and features contained in each one of them are extracted by applying kernel PCA. The use of base classifiers within an ensemble allows our system to identify the informative patterns regardless of their size or location. Decisions of each individual patch are then combined according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario where distinguishing between pneumonia patients and controls from chest Computed Tomography (CCT) images, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would highly increase the computational cost) evidence the applicability of our proposal in a real-world environment.
PB IEEE
YR 2022
FD 2022-10-10
LK https://hdl.handle.net/10630/28105
UL https://hdl.handle.net/10630/28105
LA eng
NO J. E. Arco et al., "Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 5, pp. 1431-1441, Oct. 2023, doi: 10.1109/TETCI.2022.3210582.
NO This work was supported by projects PGC2018-098813-B-C32 and RTI2018- 098913-B100 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086, A-TIC-080-UGR18 and P20 00525 (Consejería de economía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF); and by Spanish ”Ministerio de Universidades” through Margarita Salas grant to J.E. Arco.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 13 abr 2026