Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning

• URL: http://arxiv.org/abs/2402.00593v2
• Date: Fri, 16 Feb 2024 15:45:53 GMT
• Title: Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning
• Authors: Ariadna Jim\'enez-Partinen, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodr\'iguez-Capit\'an, Ana I. Molina-Ramos
• Abstract summary: Invasive Coronary Angiography (ICA) images are considered the gold standard for assessing the state of the coronary arteries. Deep learning classification methods are widely used and well-developed in different areas.
• Score: 0.6749750044497731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Invasive Coronary Angiography (ICA) images are considered the gold standard for assessing the state of the coronary arteries. Deep learning classification methods are widely used and well-developed in different areas where medical imaging evaluation has an essential impact due to the development of computer-aided diagnosis systems that can support physicians in their clinical procedures. In this paper, a new performance analysis of deep learning methods for binary ICA classification with different lesion degrees is reported. To reach this goal, an annotated dataset of ICA images that contains the ground truth, the location of lesions and seven possible severity degrees ranging between 0% and 100% was employed. The ICA images were divided into 'lesion' or 'non-lesion' patches. We aim to study how binary classification performance is affected by the different lesion degrees considered in the positive class. Therefore, five known convolutional neural network architectures were trained with different input images where different lesion degree ranges were gradually incorporated until considering the seven lesion degrees. Besides, four types of experiments with and without data augmentation were designed, whose F-measure and Area Under Curve (AUC) were computed. Reported results achieved an F-measure and AUC of 92.7% and 98.1%, respectively. However, lesion classification is highly affected by the degree of the lesion intended to classify, with 15% less accuracy when <99% lesion patches are present.

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