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. 2021 Sep;16(9):1537-1548.
doi: 10.1007/s11548-021-02414-0. Epub 2021 Jun 7.

Liver disease classification from ultrasound using multi-scale CNN

Hui Che  1 , Lloyd G Brown  2 , David J Foran  3 , John L Nosher  4 , Ilker Hacihaliloglu  5   6
Affiliations

Liver disease classification from ultrasound using multi-scale CNN

Hui Che et al. Int J Comput Assist Radiol Surg. 2021 Sep.

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 ([Formula: see text]).

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.

Keywords: Classification; Deep learning; Nonalcoholic fatty liver disease; Ultrasound.

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