Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis

• Published in: Annals of operations research 2021-07, p.1-21
• Main Authors: Qayyum, Abdul, Razzak, Imran, Tanveer, M, Kumar, Ajay
• Format: Article
• Language: English
• Subjects: Biological Physics; Original Research; Physics
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-021-04154-5

Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.