Abstract
Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes. And each subtype has different treatment methods. An accurate diagnosis is the prerequisite for effective HCE treatment. However, clinicians with less diagnostic experience often make misdiagnoses of HCE and confuse its 5 subtypes in clinical practice. Computer-aided diagnosis (CAD) techniques can help clinicians to improve their diagnostic performance. This paper aims to propose an efficient CAD system that automatically differentiates 5 subtypes of HCE from the ultrasound images. The proposed CAD system adopts the concept of deep transfer learning and uses a pre-trained convolutional neural network (CNN) named VGG19 to extract deep CNN features from the ultrasound images. The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed. The experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. The overall classification accuracy from low to high was 90.46 ± 1.59% for KNN classifier, 90.92 ± 2.49% for transfer learned VGG19, and 92.01 ± 1.48% for SVM, indicating SVM classifiers with deep CNN features achieved the best performance (P < 0.05). Other performance measures used in the study are specificity, sensitivity, precision, F1-score, and area under the curve (AUC). In addition, the paper addresses a practical aspect by evaluating the system with smaller training data to demonstrate the capability of the proposed classification system. The observations of the study imply that transfer learning is a useful technique when the availability of medical images is limited. The proposed classification system by using deep CNN features and SVM classifier is potentially helpful for clinicians to improve their HCE diagnostic performance in clinical practice.