Lung Infection Segmentation of COVID-19 in CT Images With Deep Convolutional Neural Network

Lung Infection Segmentation of COVID-19 in CT Images With Deep Convolutional Neural Network

Publication Date : 2023-08-05
Author(s) :

Mr. Jijo G, Mrs. Sukanya S. T
Conference Name :

The International Conference on scientific innovations in Science, Technology, and Management (NGCESl-2023)
Abstract :

In this paper, try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections as well as the entire lung from chest CT images, referred to as COVID-SegNet. The proposed method can be hugely beneficial for the early screening of patients with COVID-19. Inspired by the observation in annotation processing, the boundaries of COVID-19 infection regions are highlighted by adjusting the window breadth and window locations, extend Squeeze and excitation (SE) unit, named Feature Variation (FV) block, for handling the confusing boundaries. The main idea of the FV block is to implicitly enhance the contrast and adjust the intensity in the feature level automatically and adaptively for different images. Based on the captured features of previous layers, the FV block employs channel attention to obtain the global parameter to generate new features. In addition to the channel attention, the FV block uses spatial attention to guide the feature extraction from inputs in the encoder. Aggregating these features can effectively enhance the capability of feature representation for the segmentation of COVID-19. Furthermore, we propose a Progressive Atrous Spatial Pyramid Pooling (PASPP) to handle the challenging shape variations of COVID-19 infection areas. PASPP consists of a base convolution module followed by a cascade of atrous convolutional layers, which uses multistage parallel fusion branches to obtain the final features. Each atrous convolutional layer in PASPP only uses atrous filters with a reasonable dilation rate to cover different receptive fields. And by the progressively aggregated information from atrous convolutional layers, the information from multiple scales is effectively fused, which further promotes the performance of COVID-19 pneumonia segmentation. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. Here, conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection.

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