Convolutional Neural Network (CNN) in COVID-19 Detection: A Case Study with Chest CT Scan Images

Deep Learning, especially Convolutional Neural Net-works (CNN) have been performing very well for the last decade in medical image classification. CNN has already shown a great prospect in detecting COVID-19 from chest X-ray images. However, due to its three dimensional data, chest CT scan images can provide better understanding of the affected area through segmentation in comparison to the chest X-ray images. But the chest CT scan images have not been explored enough to achieve sufficiently good results in comparison to the X-ray images. However, with proper image pre-processing, fine tuning, and optimization of the models better results can be achieved. This work aims in contributing to filling this void in the literature. On this aspect, this work explores and designs both custom CNN model and three other models based on transfer learning: InceptionV3, ResNet50, and VGG19. The best performing model is VGG19 with an accuracy of 98.39% and F-1 score of 98.52%. The main contribution of this work includes: (i) modeling a custom CNN model and three pre-trained models based on InceptionV3, ResNet50, and VGG19 (ii) training and validating the models with a comparatively larger dataset of 1252 COVID-19 and 1230 non-COVID CT images (iii) fine tune and optimize the designed models based on the parameters like number of dense layers, optimizer, learning rate, batch size, decay rate, and activation functions to achieve better results than the most of the state-of-the-art literature (iv) the designed models are made public in [1] for reproducibility by the research community for further developments and improvements. © 2022 IEEE.

Diagnosis of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networking with K-Fold Cross Validation

SARS COV-2 or Novel Coronavirus COVID19 is now the world's most difficult problem;it has turned pandemic, and a large number of people have died during this pandemic era. The mortality rate is much higher than that of any other illness in the past century. A vaccination against this virus has not yet been developed. The virus is detected via RTPCR testing, which is not 100 percent reliable, is expensive, and there is a scarcity of test kits. Thus, the objective of this approach is to identify COVID-19 utilizing both a conventional system and a deep learning method with increased accuracy and availability. This approach proposes a Convolutional Neural Networking technique with 19 consecutive layers and a dataset consisting of two classes of data: COVID and Normal x-ray. The collection contains 1621 pictures, 280 of which are of COVID patients and 1341 of which are of normal patients. While training using the suggested Convolutional Neural Network architecture, the K-fold cross validation is utilized, and the folding is performed five times. Prior to incorporating pictures, some preprocessing was performed, such as via the use of an anisotropic diffusion filter. The suggested technique is 99.5 percent accurate on average. © 2021 IEEE.