RT Conference Proceedings
T1 Deep learning for coronary artery disease severity classification
A1 Jiménez-Partinen, Ariadna
A1 Thurnhofer-Hemsi, Karl
A1 Palomo-Ferrer, Esteban José
A1 Molina-Ramos, Ana I.
K1 Medicina - Innovaciones tecnológicas
AB Medical imaging evaluations are one of the fields where computed-aid diagnosis could improve the efficiency ofdiagnosis supporting physician decisions. Cardiovascular Artery Disease (CAD) is diagnosed using the gold standard, InvasiveCoronary Angiography (ICA). In this work, performance analysis for binary classification of ICA images considering theseverity ranges separately is reported, evaluating how performance is affected depending on the degree of lesions considered.For this purpose, an annotated dataset of ICA images was employed, which contains the ground truth, the location and thecategory of lesions into seven possible ranges: <20 %, [20 %, 49 %], [50 %, 69 %], [70 %, 89 %], [90 %, 98 %], 99 %, and100 %. The ICA images were pre-processed, divided into patches and balanced by downsampling and data augmentation. Inthis study, four known pre-trained CNN architectures were trained using different categories of lesion degree as input, whoseF-measures are computed. Results report that the F-measures showed a behavior dependent on the narrow presents of theimage, being lesions with more than 50 % severity were better classified, achieving an F-measure of 75%.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/26954
UL https://hdl.handle.net/10630/26954
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026