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

PeerJ Computer Science

Alex Noel Joseph Raj¹  Sushil Ghildiyal²  Ruban Nersisson²  Tharun J. Iyer² 

[1] Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China;School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India;

关键词: Convolutional neural networks;    Computed tomography;    COVID-19;    Segmentation;    High-Resolution Network (HR Net);    Segmentation Network (Seg Net);   

DOI  :  10.7717/peerj-cs.368

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown