Two-Parallel-Step CNN Framework for Detection of COVID-19 Based on Segmented CT-Scan and Chest X-Ray Images

Abstract

COVID-19 is a disease that infects people and quickly isolates the entire world. The new variants of COVID-19 continue to cause high mortality rates. Therefore, many scientists worldwide still are looking for a solution to quickly and accurately detect COVID-19. This paper aims to detect COVID-19 using chest CT-Scan and Chest X-ray images. In this work, we design a new bimodal convolutional neural network (CNN) that requires two inputs. The first modality is a chest CT-Scan image segmented by a U-Net deep learning technique to detect infected areas in the lung. The second input is a Chest X-ray image. The proposed CNN combines the features extracted from these two images. Feature extraction is performed on these two input images in two parallel feature extraction layers. The extracted feature vectors will be combined by a perceptron attention mechanism and taken as input to fully connected layers to classify the patient as COVID-19, non-COVID, and pneumonia. The results have shown that the newly designed CNN outperforms other similar state-of-the-art methods especially in distinguishing between pneumonia and COVID-19 cases. The proposed CNN has achieved 98.79% classification accuracy and 43.20% loss. The proposed framework could be particularly beneficial in telemedicine, enabling remote diagnosis in areas with limited access to medical specialists.