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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References
Alqudah, A., Alqudah, A.M.: Sliding window based deep ensemble system for breast cancer classification. J. Med. Eng. Technol. 45(4), 313–323 (2021)
Cai, L., Gao, J., Zhao, D.: A review of the application of deep learning in medical image classification and segmentation. Annals Trans. Med. 8(11) (2020)
Ciga, O., Xu, T., Nofech-Mozes, S., Noy, S., Lu, F.I., Martel, A.L.: Overcoming the limitations of patch-based learning to detect cancer in whole slide images. Sci. Rep. 11(1), 8894 (2021)
Cong, C., Kato, Y., Vasconcellos, H.D., Lima, J., Venkatesh, B.: Automated stenosis detection and classification in x-ray angiography using deep neural network. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1301–1308. IEEE (2019)
This is to inform you that corresponding author has been identified as per the information available in the Copyright form.This is to inform you that corresponding author has been identified as per the information available in the Copyright form.Danilov, V.V., et al.: Real-time coronary artery stenosis detection based on modern neural networks. Sci. Rep. 11(1), 1–13 (2021)
Eschen, C.K., et al.: Classification of left and right coronary arteries in coronary angiographies using deep learning. Electronics 11(13), 2087 (2022)
Grandini, M., Bagli, E., Visani, G.: Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756 (2020)
Haryanto, T., Suhartanto, H., Arymurthy, A.M., Kusmardi, K.: Conditional sliding windows: an approach for handling data limitation in colorectal histopathology image classification. Inform. Med. Unlocked 23, 100565 (2021)
Jiménez-Partinen, A., et al.: CADICA: a new dataset for coronary artery disease (2024)
Lam, C., Yu, C., Huang, L., Rubin, D.: Retinal lesion detection with deep learning using image patches. Investigative Ophthal. Vis. Sci. 59(1), 590–596 (2018)
Leape, L.L., Park, R.E., Bashore, T.M., Harrison, J.K., Davidson, C.J., Brook, R.H.: Effect of variability in the interpretation of coronary angiograms on the appropriateness of use of coronary revascularization procedures. Am. Heart J. 139(1), 106–113 (2000)
Lee, J., Bang, J., Yang, S.I.: Object detection with sliding window in images including multiple similar objects. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 803–806. IEEE (2017)
Litjens, G., Ciompi, F., Wolterink, J.M., de Vos, B.D., Leiner, T., Teuwen, J., Išgum, I.: State-of-the-art deep learning in cardiovascular image analysis. JACC: Cardiovascular Imaging 12(8 Part 1), 1549–1565 (2019)
Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)
Menezes, M.N., et al.: Development of deep learning segmentation models for coronary x-ray angiography: quality assessment by a new global segmentation score and comparison with human performance. Rev. Port. Cardiol. 41(12), 1011–1021 (2022)
Ovalle-Magallanes, E., Alvarado-Carrillo, D.E., Avina-Cervantes, J.G., Cruz-Aceves, I., Ruiz-Pinales, J., Correa, R.: Deep learning-based coronary stenosis detection in x-ray angiography images: Overview and future trends. In: Artificial Intelligence and Machine Learning for Healthcare: Vol. 2: Emerging Methodologies and Trends, pp. 197–223 (2022)
Ovalle-Magallanes, E., Avina-Cervantes, J.G., Cruz-Aceves, I., Ruiz-Pinales, J.: Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in x-ray images. Comput. Methods Programs Biomed. 219, 106767 (2022)
Park, J., et al.: Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography. Med. Phys. (2023)
Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29(9), 2352–2449 (2017)
Rigatelli, G., Gianese, F., Zuin, M.: Modern atlas of invasive coronary angiography views: a practical approach for fellows and young interventionalists. Int. J. Cardiovasc. Imaging 38(5), 919–926 (2022)
Yang, Y., et al.: Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image. IEEE Trans. Cybernet. 52(11), 11407–11417 (2021)
Zhou, S.K., et al.: A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc. IEEE 109(5), 820–838 (2021)
Zhou, Y., Guo, H., Song, J., Chen, Y., Wang, J.: Review of vessel segmentation and stenosis classification in x-ray coronary angiography. In: 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–5. IEEE (2021)
Zir, L.M., Miller, S.W., Dinsmore, R.E., Gilbert, J., Harthorne, J.: Interobserver variability in coronary angiography. Circulation 53(4), 627–632 (1976)
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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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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