LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,229
تعداد مقالات11,199
تعداد مشاهده مقاله77,625,812
تعداد دریافت فایل اصل مقاله102,194,771
Naderi Yaghouti, Amir Reza, Shalbaf, Ahmad. (1404). Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images. سامانه مدیریت نشریات علمی, 15(5), 437-450. doi: 10.31661/jbpe.v0i0.2307-1646
Amir Reza Naderi Yaghouti; Ahmad Shalbaf. "Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images". سامانه مدیریت نشریات علمی, 15, 5, 1404, 437-450. doi: 10.31661/jbpe.v0i0.2307-1646
Naderi Yaghouti, Amir Reza, Shalbaf, Ahmad. (1404). 'Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images', سامانه مدیریت نشریات علمی, 15(5), pp. 437-450. doi: 10.31661/jbpe.v0i0.2307-1646
Naderi Yaghouti, Amir Reza, Shalbaf, Ahmad. Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images. سامانه مدیریت نشریات علمی, 1404; 15(5): 437-450. doi: 10.31661/jbpe.v0i0.2307-1646

Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images

Journal of Biomedical Physics and Engineering
دوره 15، شماره 5 - شماره پیاپی 1، دی 2025، صفحه 437-450    اصل مقاله (1.26 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2307-1646
نویسندگان
Amir Reza Naderi Yaghouti1؛ Ahmad Shalbaf* 2
1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Nonalcoholic Fatty Liver Disease (NAFLD) as a prevalent condition can significantly have health implications. Early detection and accurate grading of NAFLD are essential for effective management and treatment of the disease.
Objective: The current study aimed to develop an advanced hybrid machine-learning model to classify NAFLD grades using ultrasound images.
Material and Methods: In this analytical study, ultrasound images were obtained from 55 highly obese individuals, who had undergone bariatric surgery and used histological results from liver biopsies as a reference for NAFLD grading. The features were extracted from the ultrasound images using popular pretrained Convolutional Neural Network (CNN) models, including VGG19, MobileNet, Xception, Inception-V3, ResNet-101, DenseNet-121, and EfficientNet-B7. The fully connected layers were removed from the CNN models and also used the remaining structure as a feature extractor. The most relevant features were then selected using the minimum Redundancy Maximum Relevance (mRMR) method. We then used four classification algorithms: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Multilayer Perceptron (MLP) neural network, and Random Forest (RF) classifiers, to categorize the ultrasound images into four groups based on liver fat level (healthy liver, low fat liver, moderate fat liver, and high-fat liver).
Results: Among the different CNN models and classification methods, EfficientNet-B7 and RF achieved the highest accuracy. The average accuracies of the LDA, MLP, SVM, and RF classifiers for the feature extraction method with EfficientNet-B7 were 88.48%, 93.15%, 95.47%, and 96.83%, respectively. The proposed automatic model can classify NAFLD grades with a remarkable accuracy of 96.83%. 
Conclusion: The proposed automatic classification model using EfficientNet-B7 for feature extraction and a Random Forest classifier can improve NAFLD diagnosis, especially in regions, in which access to professional and experienced medical experts is limited.

تازه های تحقیق

Amir Reza Naderi Yaghouti (Google Scholar)

Ahmad Shalbaf (Google Scholar)

کلیدواژه‌ها
Fatty Liver؛ Ultrasound Images؛ Liver Diseases؛ Convolutional Neural Networks؛ Random Forest
مراجع
  1. Bharath R, Rajalakshmi P. Deep scattering convolution network based features for ultrasonic fatty liver tissue characterization. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Jeju, Korea (South): IEEE; 2017. p. 1982-5.
  2. Kim KB, Kim CW. Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography. Biomed Res Int. 2015;2015:535894. doi: 10.1155/2015/535894. PubMed PMID: 26247023. PubMed PMCID: PMC4515496.
  3. Alizadeh Sani Z, Shalbaf A, Behnam H, Shalbaf R. Automatic computation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reduction. J Digit Imaging. 2015;28(1):91-8. doi: 10.1007/s10278-014-9722-z. PubMed PMID: 25059548. PubMed PMCID: PMC4305052.
  4. Shalbaf A, AlizadehSani Z, Behnam H. Echocardiography without electrocardiogram using nonlinear dimensionality reduction methods. J Med Ultrason. 2015;42(2):137-49. doi: 10.1007/s10396-014-0588-y. PubMed PMID: 26576567.
  5. Das A, Connell M, Khetarpal S. Digital image analysis of ultrasound images using machine learning to diagnose pediatric nonalcoholic fatty liver disease. Clin Imaging. 2021;77:62-8. doi: 10.1016/j.clinimag.2021.02.038. PubMed PMID: 33647632.
  6. Ribeiro R, Sanches J. Fatty liver characterization and classification by ultrasound. In: Pattern Recognition and Image Analysis; Berlin, Heidelberg: Springer Berlin Heidelberg; 2009. p. 354-61.
  7. Kyriacou E, Pavlopoulos S, Konnis G, Koutsouris D, Zoumpoulis P, Theotokas L. Computer assisted characterization of diffused liver disease using image texture analysis techniques on B-scan images. Nuclear Science Symposium Conference Record; Albuquerque, NM, USA: IEEE; 1997.
  8. Wan J, Zhou S. Features extraction based on wavelet packet transform for B-mode ultrasound liver images. 3rd International Congress on Image and Signal Processing; Yantai, China: IEEE; 2010.
  9. Acharya UR, Sree SV, Ribeiro R, Krishnamurthi G, Marinho RT, Sanches J, Suri JS. Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm. Med Phys. 2012;39(7):4255-64. doi: 10.1118/1.4725759. PubMed PMID: 22830759.
  10. Kuppili V, Biswas M, Sreekumar A, Suri HS, Saba L, Edla DR, et al. Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization. J Med Syst. 2017;41(10):152. doi: 10.1007/s10916-017-0797-1. PubMed PMID: 28836045.
  11. Naderi Yaghouti AR, Shalbaf A, Maghsoudi A. Automatic classification of Non-alcoholic fatty liver using texture features from ultrasound images. Tehran-Univ-Med-J. 2021;79(1):10-7.
  12. Hassan TM, Elmogy M, Sallam ES. Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arab J Sci Eng. 2017;42(8):3127-40. doi: 10.1007/s13369-016-2387-9.
  13. Saba L, Dey N, Ashour AS, Samanta S, Nath SS, Chakraborty S, et al. Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm. Comput Methods Programs Biomed. 2016;130:118-34. doi: 10.1016/j.cmpb.2016.03.016. PubMed PMID: 27208527.
  14. Song T, Yu X, Yu S, Ren Z, Qu Y. Feature extraction processing method of medical image fusion based on neural network algorithm. 2021;2021:1-10. doi: 10.1155/2021/7523513.
  15. Reddy DS, Bharath R, Rajalakshmi P. Classification of nonalcoholic fatty liver texture using convolution neural networks. 20th International Conference on e-Health Networking, Applications and Services (Healthcom); Ostrava, Czech Republic: IEEE; 2018. p. 1-5.
  16. Salehi AW, Khan S, Gupta G, Alabduallah BI, Almjally A, Alsolai H, et al. A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. 2023;15(7):5930. doi: 10.3390/su15075930.
  17. Byra M, Styczynski G, Szmigielski C, Kalinowski P, Michałowski Ł, Paluszkiewicz R, et al. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg. 2018;13(12):1895-903. doi: 10.1007/s11548-018-1843-2. PubMed PMID: 30094778. PubMed PMCID: PMC6223753.
  18. Constantinescu EC, Udriștoiu AL, Udriștoiu ȘC, Iacob AV, Gruionu LG, Gruionu G, et al. Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images. Medical Ultrasonography. 2021;23(2):135-9. doi: 10.11152/mu-2746.
  19. Dataset of B-mode fatty liver ultrasound images. 2018. Available from: https://zenodo.org/records/1009146.
  20. Wang D, Fang Y, Hu B, Cao H. B-scan image feature extraction of fatty liver. Sixth International Conference on Internet Computing for Science and Engineering; Zhengzhou, China: IEEE; 2012.
  21. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [Internet]. arXiv [Preprint]. 2014 [cited 2014 Sep 4]. Available from: https://arxiv.org/abs/1409.1556.
  22. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition; CVPR; 2015. p: 1-9.
  23. Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev. 2021;54:811-41. doi: 10.1007/s10462-020-09865-y.
  24. Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition; CVPR; 2017. p. 1251-8.
  25. Arcos-García Á, Álvarez-García JA, Soria-Morillo LM. Evaluation of deep neural networks for traffic sign detection systems. Neurocomputing. 2018;316:332-44. doi: 10.1016/j.neucom.2018.08.009.
  26. Nugroho BA. An aggregate method for thorax diseases classification. Sci Rep. 2021;11(1):3242. doi: 10.1038/s41598-021-81765-9. PubMed PMID: 33547338. PubMed PMCID: PMC7864910.
  27. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning; PMLR; 2019. p. 6105-14.
  28. Azadi H, Akbarzadeh-T MR, Kobravi HR, Sarcheshmeh AN, Shahsavanpour N, Asgharzade MR. Presentation of a new gender dependent feature selection approach for diagnosis of Parkinson disease using speech signal processing. International congress on technology, communication and knowledge (ICTCK); Mashhad, Iran: IEEE; 2015. p. 371-5.
  29. Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1226-38. doi: 10.1109/TPAMI.2005.159. PubMed PMID: 16119262.
  30. Torkkola K. Linear discriminant analysis in document classification. In Proceedings of the IEEE International Conference on Data Mining: Workshop on Text Mining; San Jose, CA, USA: IEEE; 2001.
  31. Jakkula V. Tutorial on support vector machine (svm). School of EECS, Washington State University; 2006.
  32. Vapnik V. The nature of statistical learning theory. Berlin: Springer; 1999.
  33. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273-97.
  34. Berzal F, Matín N. Data mining: concepts and techniques by Jiawei Han and Micheline Kamber. SIGMOD Rec. 2002;31(2):66-8. doi: 10.1145/565117.565130.
  35. Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern. 2012;42(2):513-29. doi: 10.1109/TSMCB.2011.2168604. PubMed PMID: 21984515.
  36. Breiman L. Random forests. Machine Learning. 2001;45:5-32.
  37. Hartshorn S. Machine learning with random forests and decision trees: A Visual guide for beginners. Kindle edition; 2016.
  38. Alam MS, Vuong ST. Random forest classification for detecting android malware. International conference on green computing and communications and IEEE Internet of Things and IEEE cyber, physical and social computing; Beijing, China: IEEE; 2013. p. 663-9.
آمار
تعداد مشاهده مقاله: 209,235
تعداد دریافت فایل اصل مقاله: 683,429


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب