Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.

BACKGROUND: The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. OBJECTIVE: The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. METHODS: The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. RESULT: The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. CONCLUSION: The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.

Denoising of magnetic resonance images using discriminative learning-based deep convolutional neural network.

BACKGROUND: The noise in magnetic resonance (MR) images causes severe issues for medical diagnosis purposes. OBJECTIVE: In this paper, we propose a discriminative learning based convolutional neural network denoiser to denoise the MR image data contaminated with noise. METHODS: The proposed method incorporates the use of depthwise separable convolution along with local response normalization with modified hyperparameters and internal skip connections to denoise the contaminated MR images. Moreover, the addition of parametric RELU instead of normal conventional RELU in our proposed architecture gives more stable and fine results. The denoised images were further segmented to test the appropriateness of the results. The network is trained on one dataset and tested on other dataset produces remarkably good results. RESULTS: Our proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The SSIM and PSNR showed an average improvement of (7.2 ± 0.002) % and (8.5 ± 0.25) % respectively when tested on different datasets without retaining the network. An improvement of 5% and 6% was achieved in the values of mean intersection over union (mIoU) and BF score when the denoised images were segmented for testing the relevancy in biomedical imaging applications. The statistical test suggests that the obtained results are statistically significant as p< 0.05. CONCLUSION: The denoised images obtained are more clinically suitable for medical image diagnosis purposes, as depicted by the evaluation parameters. Further, external clinical validation was performed by an experienced radiologist for testing the validation of the resulting images.

Modeling two-rate adsorption kinetics: Two-site, two-species, bilayer and rearrangement adsorption processes.

The adsorption kinetics of many systems show apparent two-rate processes, where there appears to be resolved fast and slow adsorption steps. Such non-standard adsorption processes cannot be accounted for by conventional modeling methods, motivating new approaches. In this work, we present four different models that can account for two-rate adsorption and are based upon physically realistic processes - two adsorbing species, two surface sites having different energies, bilayer formation and molecular rearrangement modes. Each model is tested using a range of conditions, and the characteristic behavior is explored and compared. In these models, the effects of mass transport and bulk concentration are also accounted for, making them applicable in systems which are transport-limited or attachment-limited, or intermediate between the two. The applicability of these models is demonstrated by fitting exemplar experimental data for each of the four models, selecting the model on the basis of the known physical behavior of the adsorption kinetics. These models can be applied in a wide range of systems, from stagnant adsorption in large volume water treatment to highly dynamic flow conditions relevant to printing, coating and processing applications.