Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet

Year 2022, Volume: 15 Issue: 1, 1 - 12, 31.01.2022

Serkan Savaş , Nurettin Topaloğlu , Ömer Kazcı Pınar Koşar

https://doi.org/10.17671/gazibtd.804617

Cited By: 9

Abstract

Deep learning is a machine learning technique that uses deep neural networks, which are multilayer neural networks that contain two or more hidden layers. In recent years, deep learning algorithms are also used to solve machine learning problems in medicine. Carotid artery disease is a type of cardiovascular disease that can result in a stroke. If a stroke is not diagnosed early, it is in the first place among the disabling diseases and the third place for the most common cause of death after cancer and heart disease. In this study, the classification performances of deep learning architectures in the biomedical field are compared, and Carotid Artery (CA) Intima-Media Thickness (IMT) Ultrasound (US) images were used. For an early diagnosis, AlexNet, ZFNet, VGGNet (16-19), which had successful results in the ImageNet competition, and authors’ original CNNcc models were used for comparison. An image database of CA-IMT-US which contains 501 ultrasound images from 153 patients was used to test the models' classification performances. It is seen that AlexNet, ZFNet, VGG16, VGG19, and CNNcc models achieved rates of 91%, 89.1%, 93%, 90%, and 89.1% respectively. The CNNcc model was found to produce successful classification results on CAIMTUS images when different performance indicators are also taken into account. In addition, different performance indicators including confusion matrices were investigated and the results were announced. The results showed that deep architectures are promising in the biomedical field and can provide proper classification on biomedical images so; this can help clinics to diagnose the disease early.

Keywords

Deep learning, image processing, carotid artery, intima-media thickness, convolutional neural network, machine learning

Thanks

The authors would like to thank the Radiology Department of Ankara Training and Research Hospital for their kindly cooperation and providing all the ultrasound images used.

Karotis Arter Intima-Medya Kalınlığı Ultrason Görüntülerinde Derin Öğrenme Modellerinin Karşılaştırılması: CAIMTUSNet

Abstract

Derin öğrenme, iki veya daha fazla gizli katman içeren çok katmanlı sinir ağları olan derin sinir ağlarını kullanan bir makine öğrenimi tekniğidir. Son yıllarda tıpta makine öğrenimi problemlerini çözmek için derin öğrenme algoritmaları da kullanılmaktadır. Karotis arter hastalığı, felçle sonuçlanabilen bir tür kardiyovasküler hastalıktır. İnme erken teşhis edilmezse, sakatlayıcı hastalıklar arasında ilk sırada, kanser ve kalp hastalıklarından sonra en sık ölüm nedeni olarak üçüncü sırada yer almaktadır. Bu çalışmada, derin öğrenme mimarilerinin biyomedikal alandaki sınıflandırma performansları karşılaştırılmış ve Karotis Arter (KA) Intima Media Thickness (IMT) Ultrason (US) görüntüleri kullanılmıştır. Erken teşhis için, ImageNet yarışmasında başarılı sonuçlar alan AlexNet, ZFNet, VGGNet (16-19) ve karşılaştırma için yazarların özgün CNNcc modelleri kullanılmıştır. 153 hastadan 501 US görüntüsünü içeren bir KA-IMT-US görüntü veritabanı, modellerin sınıflandırma performanslarını test etmek için kullanılmıştır. AlexNet, ZFNet, VGG16, VGG19 ve CNNcc modellerinin sırasıyla %91,%89.1, %93, %90 ve %89.1 oranlarına ulaştığı görülmüştür. CNNcc modelinin, farklı performans göstergeleri de hesaba katıldığında KAIMTUS görüntüleri üzerinde başarılı sınıflandırma sonuçları ürettiği bulunmuştur. Ayrıca çalışmada karışıklık matrislerini de içeren farklı performans göstergeleri incelenmiş ve sonuçlar açıklanmıştır. Sonuçlar, derin mimarilerin biyomedikal alanda ümit verici olduğunu ve biyomedikal görüntülerde uygun sınıflandırma sağlayabileceğini göstermiştir ki bu, kliniklerin hastalıkları erken teşhis etmesine yardımcı olabilir.

Keywords

Derin öğrenme, karotis arter, görüntü işleme, Karotis arter, intima-media kalınlığı, evrişimli sinir ağları, makine öğrenmesi

References

• A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, 25: 1097-1105, 2012.
• G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., 18(7): 1527-1554, 2006. doi: 10.1162/neco.2006.18.7.1527.
• K. Phil, Matlab Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence. Seoul, Soul-t'ukpyolsi, Korea: Apress, 2017.
• H. A. Song and S.-Y. Lee, "Hierarchical representation using NMF," in International conference on neural information processing, 2013: Springer, 466-473.
• S. Savaş, N. Topaloğlu, Ö. Kazcı, and P. N. Koşar, "Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning," Journal of Medical Systems, 43(8): 273, 2019, doi: 10.1007/s10916-019-1406-2.
• O. Güler and İ. Yücedağ, "Hand Gesture Recognition from 2D Images by Using Convolutional Capsule Neural Networks," Arabian Journal for Science and Engineering, 2021/06/25 2021. doi: 10.1007/s13369-021-05867-2.
• P. J. Hu, F. Wu, J. L. Peng, Y. Y. Bao, F. Chen, and D. X. Kong, "Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets," Int. J. Comput. Assist. Radiol. Surg., 12(3): 399-411, 2017. doi: 10.1007/s11548-016-1501-5.
• O. Z. Kraus, J. L. Ba, and B. J. Frey, "Classifying and segmenting microscopy images with deep multiple instance learning," Bioinformatics, Article; Proceedings Paper, 32(12): 52-59, 2016. doi: 10.1093/bioinformatics/btw252.
• O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in International Conference on Medical image computing and computer-assisted intervention, 2015: Springer, 234-241.
• D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck, "Deep learning for identifying metastatic breast cancer," arXiv preprint arXiv:1606.05718, 2016.
• D. C. Cireşan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, "Mitosis detection in breast cancer histology images with deep neural networks," in International conference on medical image computing and computer-assisted intervention, 2013: Springer, 411-418.
• V. Gulshan et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," Jama, 316(22): 2402-2410, 2016.
• Q. Dou, H. Chen, L. Q. Yu, J. Qin, and P. A. Heng, "Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection," IEEE Trans. Biomed. Eng., 64(7): 1558-1567, 2017. doi: 10.1109/tbme.2016.2613502.
• U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals," Computers in biology and medicine, 100: 270-278, 2018.
• L. A. Yeola and M. P. Satone, "Deep neural network for the automated detection and diagnosis of seizure using EEG signals," International Research Journal of Engineering and Technology (IRJET), 6(7): 381-385, 2019.
• K. Gürkahraman and R. Karakış, "Brain tumors classification with deep learning using data augmentation," Journal of the Faculty of Engineering and Architecture of Gazi University, 36(2): 997-1011, 2021, doi: 10.17341/gazimmfd.762056.
• S. Luo, X. Li, and J. Li, "Automatic Alzheimer’s disease recognition from MRI data using deep learning method," Journal of Applied Mathematics and Physics, 5(9): 1892-1898, 2017.
• W. Lin et al., "Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment," Frontiers in Neuroscience, 12(777), 2018, doi: 10.3389/fnins.2018.00777.
• Y. Ding et al., "A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain," Radiology, 290(2): 456-464, 2019. doi: 10.1148/radiol.2018180958.
• S. Savaş, "Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures," Arabian Journal for Science and Engineering, 2021/09/20, 2021, doi: 10.1007/s13369-021-06131-3.
• R. Karakış and K. Gürkahraman, "Medikal Görüntülerde Derin Öğrenme ile Steganaliz," Bilişim Teknolojileri Dergisi, 14(2): 151-159, 2021.
• B. H. Menze et al., "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)," IEEE Transactions on Medical Imaging, 34(10): 1993-2024, 2015. doi: 10.1109/TMI.2014.2377694.
• A. E. Kavur et al., "CHAOS Challenge - combined (CT-MR) healthy abdominal organ segme ntation," Medical Image Analysis, 69: 101950, 2021, doi: 10.1016/j.media.2020.101950.
• X. Zhuang et al., "Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge," Medical image analysis, 58: 101537, 2019.
• Internet: A. Civelek. "Karotis Arter Hastalığı." http://www.alicivelek.com/karotis-arter-hastaligi 1.1.2021.
• M.-G. Bousser, "Stroke prevention: an update," Frontiers of medicine, 6(1): 22-34, 2012.
• K. Strong, C. Mathers, and R. Bonita, "Preventing stroke: saving lives around the world," Lancet Neurol., 6(2): 182-187, 2007. doi: 10.1016/s1474-4422(07)70031-5.
• A. Demirci Şahin, Y. Üstü, and D. Işık, "Management of Preventable Risk Factors of Cerebrovascular Disease," Ankara Medical Journal, 15(2): 2015.
• S. Bakanlığı, Türkiye hastalık yükü çalışması 2004, Hıfzıssıhha Mektebi Müdürlüğü, 2006.
• C. C. Phatouros et al., "Carotid artery stent placement for atherosclerotic disease: Rationale, technique, and current status," Radiology, 217(1): 26-41, 2000. doi: 10.1148/radiology.217.1.r00oc2526.
• N. Daldal, Z. Cömert, and K. Polat, "Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information," Applied Soft Computing, 86: 105834, 2020. doi: 10.1016/j.asoc.2019.105834.
• M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in European conference on computer vision, 2014: Springer, 818-833.
• K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
• F. Doğan and I. Türkoğlu, "Comparison of Leaf Classification Performance of Deep Learning Algorithms," Sakarya University Journal of Computer and Information Sciences, 1: 10-21, 2018.
• R.-M. Menchón-Lara, J.-L. Sancho-Gómez, and A. Bueno-Crespo, "Early-stage atherosclerosis detection using deep learning over carotid ultrasound images," Applied Soft Computing, 49: 616-628, 2016, doi: 10.1016/j.asoc.2016.08.055.
• L. Maier-Hein et al., "BIAS: Transparent reporting of biomedical image analysis challenges," Medical image analysis, 66: 101796, 2020.
• H. Zhao, O. Gallo, I. Frosio, and J. Kautz, "Loss Functions for Image Restoration With Neural Networks," IEEE Transactions on Computational Imaging, 3(1): 47-57, 2017. doi: 10.1109/TCI.2016.2644865.
• D. Ballabio, F. Grisoni, and R. Todeschini, "Multivariate comparison of classification performance measures," Chemometrics and Intelligent Laboratory Systems, 174: 33-44, 2018.
• S. Kılıç, "ROC analysis in clinical decision making," Psychiatry and Behavioral Sciences, 3(3): 135, 2013.
• A. E. Kavur et al., "Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors," Diagnostic and Interventional Radiology, 26(1): 11, 2020. doi: 10.5152/dir.2019.19025.
• L. Maier-Hein et al., "Why rankings of biomedical image analysis competitions should be interpreted with care," Nature communications, 9(1): 1-13, 2018.
• M. Wiesenfarth et al., "Methods and open-source toolkit for analyzing and visualizing challenge results," Scientific Reports, 11(1): 2369, 2021. doi: 10.1038/s41598-021-82017-6.
• R. Rocha, A. Campilho, J. Silva, E. Azevedo, and R. Santos, "Segmentation of the carotid intima-media region in B-mode ultrasound images," Image and Vision Computing, 28(4): 614-625. 2010. doi: 10.1016/j.imavis.2009.09.017.
• M. C. Bastida-Jumilla, R. M. Menchón-Lara, J. Morales-Sánchez, R. Verdú-Monedero, J. Larrey-Ruiz, and J. L. Sancho-Gómez, "Frequency-domain active contours solution to evaluate intima–media thickness of the common carotid artery," Biomedical Signal Processing and Control, vol. 16, pp. 68-79, 2015/02/01/ 2015, doi: 10.1016/j.bspc.2014.08.012.
• U. Kutbay, F. Hardalaç, M. Akbulut, Ü. Akaslan, and S. Serhatlıoğlu, "A Computer-Aided Diagnosis System for Measuring Carotid Artery Intima-Media Thickness (IMT) Using Quaternion Vectors," J Med Syst, 40(6): 149, 2016. doi: 10.1007/s10916-016-0507-4.
• N. Ikeda et al., "Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners: Stroke risk assessment," Comput Methods Programs Biomed, 141: 73-81, 2017. doi: 10.1016/j.cmpb.2017.01.009.
• E. Kyriacou et al., "Ultrasound imaging in the analysis of carotid plaque morphology for the assessment of stroke," Stud Health Technol Inform, 113: 241-75, 2005.
• C. I. Christodoulou, C. S. Pattichis, M. Pantziaris, and A. Nicolaides, "Texture-based classification of atherosclerotic carotid plaques," IEEE Trans Med Imaging, 22(7): 902-912, 2003. doi: 10.1109/tmi.2003.815066.
• E. Kyriacou et al., "Classification of atherosclerotic carotid plaques using morphological analysis on ultrasound images," Applied Intelligence, 30(1): 3-23, 2009, doi: 10.1007/s10489-007-0072-0.
• R. U. Acharya et al., "Symptomatic vs. asymptomatic plaque classification in carotid ultrasound," J Med Syst, 36(3): 1861-1871, 2012, doi: 10.1007/s10916-010-9645-2.
• F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation," Nature Methods, 18(2): 203-211, 2021, doi: 10.1038/s41592-020-01008-z.
• A. E. Kavur, L. I. Kuncheva, and M. A. Selver, "Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation From CT Images," arXiv preprint arXiv:2001.09647, 2020.
• S. Buyrukoğlu, "Early Detection of Alzheimer’s Disease Using Data Mining: Comparision of Ensemble Feature Selection Approaches," Konya Mühendislik Bilimleri Dergisi, 9(1): 50-61, 2021, doi: 10.36306/konjes.731624.
• G. Buyrukoğlu, S. Buyrukoğlu, and Z. Topalcengiz, "Comparing Regression Models with Count Data to Artificial Neural Network and Ensemble Models for Prediction of Generic Escherichia coli Population in Agricultural Ponds Based on Weather Station Measurements," Microbial Risk Analysis, 100171, 2021. doi: 10.1016/j.mran.2021.100171.
• S. Buyrukoğlu, "New hybrid data mining model for prediction of Salmonella presence in agricultural waters based on ensemble feature selection and machine learning algorithms," Journal of Food Safety, e12903, 2021. doi: 10.1111/jfs.12903.
• S. Savaş, N. Topaloğlu, Ö. Kazcı, and P. N. Koşar, "Performance Comparison of Carotid Artery Intima Media Thickness Classification by Deep Learning Methods," presented at the International Congress on Human-Computer Interaction, Optimization, and Robotic Applications, Urgup, Nevşehir, Turkey, 2019. doi: 10.36287/setsci.4.5.025.