MFFC-Net: Multi-feature Fusion Deep Networks for Classifying Pulmonary Edema of a Pilot Study by Using Lung Ultrasound Image with Texture Analysis and Transfer Learning Technique.

Abstract

Lung ultrasound (LUS) has been widely used by point-of-care systems in both children and adult populations to provide different clinical diagnostics. This research aims to develop an interpretable system that uses a deep fusion network for classifying LUS video/patients based on extracted features by using texture analysis and transfer learning techniques to assist physicians. The pulmonary edema dataset includes 56 LUS videos and 4234 LUS frames. The COVID-BLUES dataset includes 294 LUS videos and 15,826 frames. The proposed multi-feature fusion classification network (MFFC-Net) includes the following: (1) two features extracted from Inception-ResNet-v2, Inception-v3, and 9 texture features of gray-level co-occurrence matrix (GLCM) and histogram of the region of interest (ROI); (2) a neural network for classifying LUS images with feature fusion input; and (3) four models (i.e., ANN, SVM, XGBoost, and kNN) used for classifying COVID/NON COVID patients. The training process was evaluated based on accuracy (0.9969), F1-score (0.9968), sensitivity (0.9967), specificity (0.9990), and precision (0.9970) metrics after the fivefold cross-validation stage. The results of the ANOVA analysis with 9 features of LUS images show that there was a significant difference between pulmonary edema and normal lungs (p < 0.01). The test results at the frame level of the MFFC-Net model achieved an accuracy of 100% and ROC-AUC (1.000) compared with ground truth at the video level with 4 groups of LUS videos. Test results at the patient level with the COVID-BLUES dataset achieved the highest accuracy of 81.25% with the kNN model. The proposed MFFC-Net model has 125 times higher information density (ID) compared to Inception-ResNet-v2 and 53.2 times compared with Inception-v3.