Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network

Abstract

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.

Keywords: Deeplabv3; ResNet-18; denoise convolutional neural network (DnCNN); healthcare; public health; stack sparse autoencoder deep learning model (SSAE).

FIGURE 1

Proposed research model

FIGURE 2

DnCNN model for noise removal

FIGURE 3

Semantic segmentation model with activation units

FIGURE 4

Result of segmented images input images, segmentation, ground truth

FIGURE 5

SSAE model for infected lung region classification

FIGURE 6

Autoencoder for image reconstruction

FIGURE 7

Features learning process: (a) SAE1 and (b) SAE2

FIGURE 8

Noise reduction: (a) input images and (b) after applying DnCNN

FIGURE 9

Segmentation results on COVID19 segmentation data set (a), (d) input CT images; (b) and (e) segmentation; and (c) and (f) truth annotated

FIGURE 10

Segmentation results with ground truth on POF Hospital data set (a) input; (b) Covid‐19 segmentation; and (c) truth annotated

FIGURE 11

Fine tune model configuration parameters with error rate

FIGURE 12

Confusion matrix on benchmark data sets (a) BSTI; (b) China Hospital; (c) POF hospital; and (d) COVID19 segmentation

FIGURE 13

ROC on benchmark data sets (a) POF hospital; (b) China Hospital; (c) BSTI; and (d) COVID‐19 segmentation