SEGMENTATION AND CLASSIFICATION OF COVID-19 MEDICAL IMAGES USING DEEP NEURAL NETWORK

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. In this project, 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. Besides using local binary pattern for feature extraction and its classification using DAENN. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. 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. 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