Automatic classification of COVID-19 infected patients using convolution neural network models

Coronavirus (COVID-19) has infected millions of people and continues to have a disastrous impact on the economy and health. Timely diagnosis of the COVID-19 infection can help contain the virus and prevent much loss of life. The COVID-19 diagnosis can be achieved by the Reverse Transcript Polymerase Chain Reaction test (RT-PCR) but it has a high false-negative rate and has low sensitivity as compared to Computed Tomography (CT) and X-Ray images. In this study, we have trained six different architectures of the Convolution Neural Network (CNN) model to detect COVID-19. We tried to identify the most efficient CNN model based on accuracy and the number of trainable parameters. The model has been trained on a Chest X-Ray image dataset retrieved from the GitHub platform with 1811 images in the training dataset and 484 images in the validation dataset. The model with the highest accuracy has been trained for a variable number of epochs varying the filter size. It has been demonstrated that architecture 3 can achieve 99% accuracy for 500 epochs with a minimum number of trainable parameters. Using just a simple CNN architecture that can be deployed in any rural healthcare center we can achieve a high level of accuracy for classification with the added advantage of less complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

LSB Technique-Based Dual-Image Steganography Using COS Function

Due to the advancement in technology, the exchange of information via a network has been taking such a great peak that each and every person is highly dependent on the Internet, like in the current Covid-19 pandemic situation. But sharing information/secret medical data by means of the Internet has created a risk to the information loss. Thus, data exchange should be done in such a way that its presence cannot be perceived by the third party with bad intentions and should not be able to modify or alter the important message. For the safety of important data, many algorithms like cryptography, steganography, watermarking, etc. have been used by researchers to safeguard data sharing via an insecure Internet. Cryptography and steganography together can provide better security to the data as one encrypts the data and the other hides its presence in multimedia, like image, audio, video, etc. In this paper, data encryption and image steganography have been presented to provide security to the message that is being transferred through the Internet. The experimental outcomes show that the proposed technique is able to transfer important data securely without coming into the eyes of a hacker. The PSNR value for the proposed system is about 51.14 with 1 bit per pixel (bpp) capacity. As per the security view, the presented method has a 7.99 entropy value, which means the system is secure against attacks.