Liver disease classification from ultrasound using multi-scale CNN

Hui Che, Lloyd G. Brown, David J. Foran, John L. Nosher, Ilker Hacihaliloglu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Purpose: Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses. Methods: In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods. Results: Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures (p< 0.05). Conclusions: Feature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.

Original languageEnglish (US)
Pages (from-to)1537-1548
Number of pages12
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume16
Issue number9
DOIs
StatePublished - Sep 2021

All Science Journal Classification (ASJC) codes

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Keywords

  • Classification
  • Deep learning
  • Nonalcoholic fatty liver disease
  • Ultrasound

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