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Deep Learning-Based Classification of Invasive Coronary Angiographies with Different Patch-Generation Techniques

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Bioinspired Systems for Translational Applications: From Robotics to Social Engineering (IWINAC 2024)

Abstract

Medical imaging is one of the areas where computer-aided diagnosis could improve the efficiency of diagnosis in clinical settings. Cardiovascular artery disease (CAD) is diagnosed by invasive coronary angiography (ICA). This paper reports on performance analysis for binary classification of ICA images by grouping severity ranges and evaluates how performance is affected by the degree of lesions and the patch generation technique considered. An annotated dataset of ICA images was used, categorizing lesions into seven possible ranges: <20%, [20%, 50%), [50%, 70%), [70%, 90%), [90%, 98%], 99% and 100%. In this study, three pre-trained CNN architectures were trained using different categories of lesion severity as input, and their F-measures and accuracy were computed, achieving a performance above 90%.

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Acknowledgments

This work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA20-FEDERJA-108, and also by the Ministry of Science and Innovation of Spain, grant number PID2022-136764OA-I00. It includes funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Málaga (Spain) under grants B1-2019_01, B1-2019_02, B1-2021_20, B4-2022, B1-2022_14, and by the Fundación Unicaja under project PUNI-003_2023. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 GPU with 48Gb. The authors also thankfully acknowledge the grant of the Universidad de Málaga and the Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND.

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Correspondence to Ariadna Jiménez-Partinen .

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Jiménez-Partinen, A., Palomo, E.J., Thurnhofer-Hemsi, K., Rodríguez-Capitán, J., Molina-Ramos, A.I. (2024). Deep Learning-Based Classification of Invasive Coronary Angiographies with Different Patch-Generation Techniques. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_12

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  • DOI: https://doi.org/10.1007/978-3-031-61137-7_12

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  • Online ISBN: 978-3-031-61137-7

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