Improving Computer-Aided Medical Diagnosis Using Generative Adversarial Networks for Carotid Artery Ultrasound Image Data Augmentation and Classification
AbstractCardiovascular diseases are a leading cause of death globally, making early detection of atherosclerosis critical for prevention. Carotid artery ultrasound imaging is a common diagnostic tool; however, the limited availability of labelled medical images hinders the training of deep learning models. This study examines generative adversarial networks (GANs) for data augmentation and classification of carotid artery Doppler images to improve computer-aided medical diagnosis. Four convolutional neural networks (CNNs) – AlexNet, VGGNet, GoogleNet, and CifarNet – are evaluated for their classification performance on original and extended datasets. AlexNet outperforms the other models, achieving a classification accuracy of 94.18% on the extended dataset. The GAN implementation for data augmentation and overfitting reduction demonstrates the potential of generative models in enhancing the performance of deep learning models in medical image analysis, particularly in the "common artery carotid" class. This research contributes to understanding GANs as a valuable tool for data augmentation and classification in the context of carotid artery ultrasound images.
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I. Poledník, J. Sulzenko, and P. Widimsky, “Risk of a coronary event in patients after ischemic stroke or transient ischemic attack,” Anatol. J. Cardiol., vol. 25, no. 3, pp. 152–155, 2021, doi: 10.5152/AnatolJCardiol.2021.75548.
G. A. Roth et al., “Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015.,” J. Am. Coll. Cardiol., vol. 70, no. 1, pp. 1–25, Jul. 2017, doi: 10.1016/j.jacc.2017.04.052.
S. Fu, F. Yan, and Y. Long, “A new automatic segmentation algorithm for carotid artery ultrasound images,” in 2022 International Seminar on Computer Science and Engineering Technology (SCSET), 2022, pp. 110–113. doi: 10.1109/SCSET55041.2022.00033.
E. Ukwatta, D. Buchanan, G. Parraga, and A. Fenster, “Three-dimensional Ultrasound Imaging of Carotid Atherosclerosis,” 2011 Int. Conf. Intell. Comput. Bio-Medical Instrum., pp. 81–84, 2011.
J. H. Gagan et al., “Automated Segmentation of Common Carotid Artery in Ultrasound Images,” IEEE Access, vol. 10, pp. 58419–58430, 2022, doi: 10.1109/ACCESS.2022.3179402.
R.-M. Menchón-Lara and J.-L. Sancho-Gómez, “Fully automatic segmentation of ultrasound common carotid artery images based on machine learning,” Neurocomputing, vol. 151, pp. 161–167, 2015, doi: https://doi.org/10.1016/j.neucom.2014.09.066.
D. Samiappan and V. Chakrapani, “Classification of carotid artery abnormalities in ultrasound images using an artificial neural classifier,” Int. Arab J. Inf. Technol., vol. 13, pp. 756–762, 2016.
C. F. Castro, R. Fitas, and E. Azevedo, “AUTOMATIC SEGMENTATION IN TRANSVERSE ULTRASOUND B- MODE IMAGES OF THE CAROTID ARTERY,” vol. 30, pp. 71–76, 2018.
R. Fitas, C. Castro, L. Sousa, C. António, R. Santos, and E. Azevedo, “Analysis of Sequential Transverse B-Mode Ultrasound Images of the Carotid Artery Bifurcation,” 2019, pp. 521–530. doi: 10.1007/978-3-030-32040-9_53.
A. Anand and N. R. Gurram, “Automated Deep Learning-based Single-Step Diameter Estimation of Carotid Arteries in B-mode Ultrasound.,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Int. Conf., vol. 2022, pp. 434–437, Jul. 2022, doi: 10.1109/EMBC48229.2022.9871254.
C. M. Bishop, “Neural networks and their applications,” Rev. Sci. Instrum., vol. 65, no. 6, pp. 1803–1832, 1994, doi: 10.1063/1.1144830.
M. H. Hesamian, W. Jia, X. He, and P. Kennedy, “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges,” J. Digit. Imaging, vol. 32, no. 4, pp. 582–596, 2019, doi: 10.1007/s10278-019-00227-x.
S. Jamroziński and U. Markowska-Kaczmar, “Semi-supervised classifier guided by discriminator,” Sci. Rep., vol. 12, no. 1, 2022, doi: 10.1038/s41598-022-18947-6.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Curran Associates Inc., 2012, pp. 1097–1105.
V. Sandfort, K. Yan, P. J. Pickhardt, and R. M. Summers, “Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks,” Sci. Rep., vol. 9, no. 1, p. 16884, 2019, doi: 10.1038/s41598-019-52737-x.
D. Kumar, M. A. Mehta, and I. Chatterjee, “Empirical Analysis of Deep Convolutional Generative Adversarial Network for Ultrasound Image Synthesis,” Open Biomed. Eng. J., vol. 15, pp. 71 – 77, 2021, doi: 10.2174/1874120702115010071.
G. Litjens et al., “A survey on deep learning in medical image analysis,” Med. Image Anal., vol. 42, pp. 60–88, 2017.
Y. Zhao et al., “Surgical GAN: Towards real-time path planning for passive flexible tools in endovascular surgeries,” Neurocomputing, vol. 500, pp. 567 – 580, 2022, doi: 10.1016/j.neucom.2022.05.044.
M. Engin et al., “AGAN: An Anatomy Corrector Conditional Generative Adversarial Network,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12262 LNCS, pp. 708 – 717, 2020, doi: 10.1007/978-3-030-59713-9_68.
H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging, vol. 35, pp. 1285–1298, 2016.
H. R. Roth et al., “Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1170–1181, 2016, doi: 10.1109/TMI.2015.2482920.
A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images,” 2009.
C. Szegedy et al., “Going Deeper With Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
C. Park, S. Lim, D. Cha, and J. Jeong, “Fv-AD: F-AnoGAN Based Anomaly Detection in Chromate Process for Smart Manufacturing,” Appl. Sci., vol. 12, no. 15, 2022, doi: 10.3390/app12157549.
I. Wahlang et al., “Deep learning methods for classification of certain abnormalities in echocardiography,” Electron., vol. 10, no. 4, pp. 1 – 20, 2021, doi: 10.3390/electronics10040495.
H. Roth et al., “Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation,” IEEE Trans. Med. Imaging, vol. 35, 2015, doi: 10.1109/TMI.2015.2482920.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” CoRR, vol. abs/1409.1, 2014.
I. Goodfellow et al., “Generative Adversarial Nets,” in Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger, Eds., Curran Associates, Inc., 2014. [Online]. Available: https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
I. Goodfellow, “On distinguishability criteria for estimating generative models,” 2014.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” CoRR, vol. abs/1412.6, 2014.
T. Salimans et al., “Improved Techniques for Training GANs,” in Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., Curran Associates, Inc., 2016. [Online]. Available: https://proceedings.neurips.cc/paper/2016/file/8a3363abe792db2d8761d6403605aeb7-Paper.pdf
C. Hacking and J. Jones, “Internal carotid artery,” Radiopaedia.org. Radiopaedia.org, 2008. doi: 10.53347/rID-4524.
O. H. Hamid, “From Model-Centric to Data-Centric AI: A Paradigm Shift or Rather a Complementary Approach?,” in 2022 8th International Conference on Information Technology Trends (ITT), 2022, pp. 196–199. doi: 10.1109/ITT56123.2022.9863935.
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Is object localization for free? - Weakly-supervised learning with convolutional neural networks,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 685–694. doi: 10.1109/CVPR.2015.7298668.
M. A. Nielsen, “Neural_Networks_and_Deep_Learning”.
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1717–1724. doi: 10.1109/CVPR.2014.222.
J. Shin, N. Tajbakhsh, R. T. Hurst, C. Kendall, and J. Liang, “Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks,” 2017.
C. P. Behrenbruch, S. Petroudi, S. Bond, J. D. Declerck, F. J. Leong, and J. M. Brady, “Image filtering techniques for medical image post-processing: an overview,” Br. J. Radiol., vol. 77, no. suppl_2, pp. S126–S132, 2004, doi: 10.1259/bjr/17464219.
A. Kumar and S. S. Sodhi, “Comparative Analysis of Gaussian Filter, Median Filter and Denoise Autoenocoder,” in 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), 2020, pp. 45–51. doi: 10.23919/INDIACom49435.2020.9083712.
S. Jagannathan, “FPGA Implementation of EPDT for Real-Time Removal of Salt and Pepper Noise,” 2011.
P.-H. Lin, B.-H. Chen, F.-C. Cheng, and S.-C. Huang, “A Morphological Mean Filter for Impulse Noise Removal,” J. Disp. Technol., vol. 12, no. 4, pp. 344–350, 2016, [Online]. Available: https://opg.optica.org/jdt/abstract.cfm?URI=jdt-12-4-344.
Setyobudi, R. (2023). Utilization of tds sensors for water quality monitoring and water filtering of carp pools using IoT. EUREKA: Physics and Engineering, (6), 69-77.
Y. Jiao, C. Qian, and S. Fei, “Mask Convolution for Filtering on Irregular-Shaped Image,” in 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 2018, pp. 115–118. doi: 10.1109/DCABES.2018.00039.
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