A Novel Computer-Aided Diagnosis Framework Using Deep Learning for Classification of Fatty Liver Disease in Ultrasound Imaging

Fatty Liver Disease (FLD), if left untreated can progress into fatal chronic diseases (Eg. fibrosis, cirrhosis, liver cancer, etc.) leading to permanent liver failure. Doctors usually use ultrasound scanning as the primary modality for quantifying the amount of fat deposition in the liver tissues, to categorize the FLD into normal and abnormal. However, this quantification or diagnostic accuracy depends on the expertise and skill of the radiologist. With the advent of Health 4.0 and the Computer Aided Diagnosis (CAD) techniques, the accuracy in detection of FLD using the ultrasound by the sonographers and clinicians can be improved. Along with an accurate diagnosis, the CAD techniques will help radiologists to diagnose more patients in less time. Hence, to improve the classification accuracy of FLD using ultrasound images, we propose a novel CAD framework using convolution neural networks and transfer learning (pre-trained VGG-16 model). Performance analysis shows that the proposed framework offers an FLD classification accuracy of 90.6% in classifying normal and fatty liver images.

[1]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[2]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[3]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[4]  Ernesto Roldan-Valadez,et al.  Imaging techniques for assessing hepatic fat content in nonalcoholic fatty liver disease. , 2008, Annals of hepatology.

[5]  José Silvestre Silva,et al.  Classifier Approaches for Liver Steatosis using Ultrasound Images , 2012 .

[6]  J. Maxwell,et al.  Ultrasound scanning in the detection of hepatic fibrosis and steatosis. , 1986, British medical journal.

[7]  Satoshi Suzuki,et al.  A Transfer Learning Method with Deep Convolutional Neural Network for Diffuse Lung Disease Classification , 2015, ICONIP.

[8]  Anjan Gudigar,et al.  Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images , 2016, Inf. Fusion.

[9]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  G. Bedogni,et al.  Epidemiology of Non-Alcoholic Fatty Liver Disease , 2010, Digestive Diseases.

[11]  Fayyaz ul Amir Afsar Minhas,et al.  Automated Classification of Liver Disorders using Ultrasound Images , 2012, Journal of Medical Systems.

[12]  Ioan Sporea,et al.  Computer aided diagnosis method for steatosis rating in ultrasound images using random forests. , 2013, Medical ultrasonography.

[13]  Mandeep Singh,et al.  A New Quantitative Metric for Liver Classification from Ultrasound Images , 2012 .

[14]  Savita Gupta,et al.  An information fusion based method for liver classification using texture analysis of ultrasound images , 2014, Inf. Fusion.

[15]  R. Torella,et al.  The role of bright liver echo pattern on ultrasound B-mode examination in the diagnosis of liver steatosis. , 2006, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

[16]  Hao Chen,et al.  Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks , 2015, IEEE Journal of Biomedical and Health Informatics.

[17]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Atsuto Maki,et al.  From generic to specific deep representations for visual recognition , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[19]  Christoph Thuemmler,et al.  Health 4.0: How Virtualization and Big Data are Revolutionizing Healthcare , 2017 .

[20]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[21]  Metin Nafi Gürcan,et al.  Quantification of liver fat: A comprehensive review , 2016, Comput. Biol. Medicine.

[22]  U Rajendra Acharya,et al.  Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm. , 2012, Medical physics.

[23]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[24]  D. Mittal,et al.  Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging , 2017, Ultrasonic imaging.

[25]  Guy Amit,et al.  Classification of breast lesions using cross-modal deep learning , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).