Acquiring the Ability to Identifying Covid19 using Deep CNN from Impulse Noise in Chest X-Ray Pictures - Manoranjan Kumar Sinha, Jayesh Surana, Sandeep Kumar Mathariya, Mahaveer Jain, Piyush Chouhan (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Utilizing CNNs, COVID19 is identified in X-ray pictures. Deep CNNs may have a harder time identifying things in noisy X-ray pictures. We provide a unique CNN technique that eliminates the need for preprocessing of noise in X-ray pictures by using adaptive convolution to enhance COVID19 detection. A CNN will therefore be more resistant to erratic noise. This method adds an adaptive convolution layer, an impulsive noise-map layer, and an adjustable scaling layer to the standard CNN architecture. Additionally, we employed a learning-to-augment technique with X-ray pictures that were noisy in order to enhance a deep CNN's generalization. The 2093 chest X-ray photos are divided into 1020 images showing a healthy image, 621 images showing pneumonia other than COVID-19, and 452 images showing COVID19. The architectures of pre-trained networks have been modified to increase their resilience to impulsive noise. Validation on noisy X-ray pictures showed that the proposed noise-robust layers and learning-to-augment strategy incorporated ResNet_50 led to 2% better classification accuracy than the present-day method..