Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images

Abstract

The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations in the types of infections and low contrast between healthy and infected tissues. While segmenting Lung CT Scans for COVID-19, fast and accurate results are required and furthermore, due to the pandemic, most of the research community has opted for various cloud based servers such as Google Colab, etc. to develop their algorithms. High accuracy can be achieved using Deep Networks but the prediction time would vary as the resources are shared amongst many thus requiring the need to compare different lightweight segmentation model. To address this issue, we aim to analyze the segmentation of COVID-19 using four Convolutional Neural Networks (CNN). The images in our dataset are preprocessed where the motion artifacts are removed. The four networks are UNet, Segmentation Network (Seg Net), High-Resolution Network (HR Net) and VGG UNet. Trained on our dataset of more than 3,000 images, HR Net was found to be the best performing network achieving an accuracy of 96.24% and a Dice score of 0.9127. The analysis shows that lightweight CNN models perform better than other neural net models when to segment infectious tissue due to COVID-19 from CT slices.

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

“…Various research using deep learning algorithms based on chest CT imaging to identify and segment COVID-19 instances from non-COVID-19 cases have been published [ 72 , 73 , 74 , 75 ]. However, most of the studies lack in individual lung area estimation, transparency overlay generation, and usage of HDL.…”

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans

“…Several studies have been published that use deep learning algorithms based on chest CT imaging to identify and segment COVID-19 lesions [73,[106][107][108]. However, most investigations lack lesion area measurement, transparency overlay generation, HDL utilization, and interobserver analysis.…”

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

“…Aresta et al, [168] Lessmann et al, [207] Zhang et al, [177] Zhu et al, [146] Ryan et al, [120] Hofmanninger et al, [4] Song et al, [207] Anastasopoulos et al, [238] Chung et al, [105] El-Bana et al, [192] Kamal et al, [218] Wang et al, [237] Zhou et al, [188] Wu et al, [190] Iyer et al, [163] Singh et al, [202] Chatzitofis et al, [217] Saood et al, [165] Raj et al, [214]…”