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Zamanian, H, Mostaar, A, Azadeh, P, Ahmadi, M. (1399). Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images. سامانه مدیریت نشریات علمی, 11(1), 73-84. doi: 10.31661/jbpe.v0i0.2009-1180
H Zamanian; A Mostaar; P Azadeh; M Ahmadi. "Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images". سامانه مدیریت نشریات علمی, 11, 1, 1399, 73-84. doi: 10.31661/jbpe.v0i0.2009-1180
Zamanian, H, Mostaar, A, Azadeh, P, Ahmadi, M. (1399). 'Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images', سامانه مدیریت نشریات علمی, 11(1), pp. 73-84. doi: 10.31661/jbpe.v0i0.2009-1180
Zamanian, H, Mostaar, A, Azadeh, P, Ahmadi, M. Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images. سامانه مدیریت نشریات علمی, 1399; 11(1): 73-84. doi: 10.31661/jbpe.v0i0.2009-1180

Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 11، شماره 1، فروردین و اردیبهشت 2021، صفحه 73-84    اصل مقاله (1.2 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2009-1180
نویسندگان
H Zamanian1؛ A Mostaar* 2، 3؛ P Azadeh4؛ M Ahmadi2
1MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2PhD, Department of Medical Physics and Biomedical Engineering and, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3PhD, Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
4MD, Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Nowadays, fatty liver is one of the commonly occurred diseases for the liver which can be observed generally in obese patients. Final results from a variety of exams and imaging methods can help to identify and evaluate people affected by this condition.
Objective: The aim of this study is to present a combined algorithm based on neural networks for the classification of ultrasound ‎images from fatty liver affected patients.
Material and Methods: In experimental research can be categorized as a diagnostic study which focuses on classification of the acquired ultrasonography images for 55 patients with fatty liver. We implemented pre-trained convolutional neural networks of Inception-ResNetv2, GoogleNet, AlexNet, and ResNet101 to extract features from the images and after combining these resulted features, we provided support vector machine (SVM) algorithm to classify the liver images. Then the results are compared with the ones in implementing the algorithms independently.
Results: The area under the receiver operating characteristic curve (AUC) for the introduced combined network resulted in 0.9999, which is a better result compared to any of the other introduced algorithms. The resulted accuracy for the proposed network also caused 0.9864, which seems acceptable accuracy for clinical application.
Conclusion: The proposed network can be used with high accuracy to classify ultrasound images of the liver to normal or fatty. The presented approach besides the high AUC in comparison with other methods have the independence of the method from the ‎user or expert interference.
کلیدواژه‌ها
Fatty Liver؛ Ultrasonography؛ Deep Learning؛ Transfer Learning؛ Support Vector Machine؛ Receiver Operating Characteristic Curve
مراجع
  1. Chalasani N, Younossi Z, Lavine JE, Diehl AM, et al. The diagnosis and management of non-alcoholic fatty liver disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology. 2012;55(6):2005-23. doi: 10.1002/hep.25762. PubMed PMID: 22488764.
  2. Koncarevic A, Lachey J, Seehra J, Sherman ML., inventors. Acceleron Pharma Inc, assignee. Methods for treating fatty liver disease. US patent. 15/148,162. 2016 Nov 3.
  3. Litjens G, Kooi T, Bejnordi BE, Setio AA, et al. A survey on deep learning in medical image analysis. Medical Image Analysis. 2017;42:60-88. doi: 10.1016/j.media.2017.07.005. PubMed PMID: 28778026.
  4. Ganapathy N, Swaminathan R, Deserno TM. Deep learning on 1-D biosignals: a taxonomy-based survey. Yearbook of Medical Informatics. 2018;27(1):98. doi: 10.1055/s-0038-1667083. PubMed PMID: 30157512. PubMed PMCID: PMC6115218.
  5. Vanderbeck S, Bockhorst J, Komorowski R, Kleiner DE, Gawrieh S. Automatic classification of white regions in liver biopsies by supervised machine learning. Human Pathology. 2014;45(4):785-92. doi: 10.1016/j.humpath.2013.11.011. PubMed PMID: 24565203.
  6. Hashem EM, Mabrouk MS. A Study of Support Vector Machine Algorithm for Liver Disease Diagnosis. American Journal of Intelligent Systems. 2014;4(1):9-14. doi: 10.5923/j.ajis.20140401.02.
  7. Rau HH, Hsu CY, Lin YA, Atique S, et al. Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network. Computer Methods and Programs in Biomedicine. 2016;125:58-65. doi: 10.1016/j.cmpb.2015.11.009. PubMed PMID: 26701199.
  8. Hinton G. Deep learning—a technology with the potential to transform health care. Jama. 2018;320(11):1101-2. doi: 10.1001/jama.2018.11100. PubMed PMID: 30178065.
  9. Cao GT, Shi PF, Hu B. Liver fibrosis identification based on ultrasound images captured under varied imaging protocols. J Zhejiang Univ Sci B. 2005;6(11):1107. doi: 10.1631/jzus.2005.B1107. PubMed PMID: 16252346. PubMed PMCID: PMC1390659.
  10. Acharya UR, Faust O, Sree SV, Molinari F, Suri JS. ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Comput Methods Progr Biomed. 2012;107(2):233-41. doi: 10.1016/j.cmpb.2011.10.001. PubMed PMID: 22054816.
  11. Andrade A, Silva JS, Santos J, Belo-Soares P. Classifier approaches for liver steatosis using ultrasound images. Proc Tech. 2012;5:763-70. doi: 10.1016/j.protcy.2012.09.084.
  12. Gao S, Peng Y, Guo H, Liu W, Gao T, Xu Y, Tang X. Texture analysis and classification of ultrasound liver images. Bio-Medical Materials and Engineering. 2014;24(1):1209-16. doi: 10.3233/BME-130922. PubMed PMID: 24212015.
  13. Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, et al. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Computers in Biology and Medicine. 2016;79:250-8. doi: 10.1016/j.compbiomed.2016.10.022.
  14. Kuppili V, Biswas M, Sreekumar A, Suri HS, Saba L, et al. Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization. Journal of Medical Systems. 2017;41(10):152. doi: 10.1007/s10916-017-0797-1. PubMed PMID: 28836045.
  15. Hassan TM, Elmogy M, Sallam ES. Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arabian Journal for Science and Engineering. 2017;42(8):3127-40. doi: 10.1007/s13369-016-2387-9.
  16. Liu X, Song J, Wang S, Zhao J, Chen Y. Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification. Sensors. 2017;17(1):149. doi: 10.3390/s17010149. PubMed PMID: 28098774. PubMed PMCID: PMC5298722.
  17. Bharath R, Rajalakshmi P. Deep scattering convolution network-based features for ultrasonic fatty liver tissue characterization. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Seogwipo, South Korea: IEEE; 2017. p. 1982-5. doi: 10.1109/EMBC.2017.8037239.
  18. Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, et al. Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Progr Biomed. 2018;155:165-77. doi: 10.1016/j.cmpb.2017.12.016. PubMed PMID: 29512496.
  19. Byra M, Styczynski G, Szmigielski C, Kalinowski P, et al. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Ass Rad. 2018;13(12):1895-903. doi: 10.1007/s11548-018-1843-2. PubMed PMID: 30094778. PubMed PMCID: PMC6223753.
  20. Marshall RH, Eissa M, Bluth EI, Gulotta PM, Davis NK. Hepatorenal index as an accurate, simple, and effective tool in screening for steatosis. Am J Roentgenol. 2012;199:997-1002. doi: 10.2214/AJR.11.6677. PubMed PMID: 23096171.
  21. Wu CC, Yeh WU, Hsu WD, Islam MM, et al. Prediction of fatty liver disease using machine learning algorithms. Comput Methods Progr Biomed. 2019;170:23-9. doi: 10.1016/j.cmpb.2018.12.032. PubMed PMID: 30712601.
  22. Mohaimenul Islam M, Wu CC, Poly TN, Yang HC, Li YC. Applications of machine learning in fatty live disease prediction. Stud Health Technol Inform. 2018;247:166-70. doi: 10.3233/978-1-61499-852-5-166. PubMed PMID: 29677944.
  23. Reddy DS, Bharath R, Rajalakshmi P. A novel computer-aided diagnosis framework using deep learning for classification of fatty liver disease in ultrasound imaging. International Conference on e-Health Networking, Applications and Services (Healthcom); Ostrava, Czech Republic: IEEE; 2018. doi: 10.1109/HealthCom.2018.8531118.
  24. Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic data augmentation using GAN for improved liver lesion classification. international symposium on biomedical imaging (ISBI). 2018;289-93. doi: 10.1109/ISBI.2018.8363576.
  25. Kleiner DE, Brunt EM, Van Natta M, Behling C, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41(6):1313-21. doi: 10.4196/kjpp.2019.23.6.459. PubMed PMID: 31680767. PubMed PMCID: PMC6819900.
  26. Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621. 2017.
  27. Kinnunen T, Li H. An overview of text-independent speaker recognition: From features to supervectors. Speech Communication. 2010;52(1):12-40. doi: 10.1016/j.specom.2009.08.009.
  28. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJ. A novel deep learning-based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters. 2019;125:1-6. doi: 10.1016/j.patrec.2019.03.022.
  29. Birjandi M, Ayatollahi SM, Pourahmad S, Safarpour AR. Prediction and diagnosis of non-alcoholic fatty liver disease (NAFLD) and identification of its associated factors using the classification tree method. Iranian Red Crescent Medical Journal. 2016;18(11):e32858. doi: 10.5812/ircmj.32858. PubMed PMID: 28191344. PubMed PMCID: PMC5292777.
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