Coronavirus disease (Covid-19) has been spreading all over the world and its diagnosis is attracting more research every moment. It is need of the hour to develop automated methods, which could detect this disease at its early stage, in a non-invasive way and within lesser time. Currently, medical specialists are analyzing Computed Tomography (CT), X-Ray, and Ultrasound (US) images or conducting Polymerase Chain Reaction (PCR) for its confirmation on manual basis. In Pakistan, CT scanners are available in most hospitals at district level, while X-Ray machines are available in all tehsil (large urban towns) level hospitals. Being widely used imaging modalities to analyze chest related diseases, produce large volume of medical data each moment clinical environments. Since automatic, time efficient and reliable methods for Covid-19 detection are required as alternate methods, therefore an automatic method of Covid-19 detection using Convolutional Neural Networks (CNN) has been proposed. Three publically available and a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur (BVHB), Pakistan have been used. The proposed method achieved on average accuracy (96.68%), specificity (95.65%), and sensitivity (96.24%). Proposed model is trained on a large dataset and is being used at the Radiology Department, (BVHB), Pakistan.

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Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.

We report the use of M mode swept source optical coherence tomography (SS OCT) for measur ing sodium chloride (NaCl) salt concentrations in liquid phantoms and in drawn whole blood based on tem poral dynamics of light scattering. The Brownian motion of scattering particle is affected due to the change in viscosity of liquid. An autocorrelation function was determined from the power spectrum of SS OCT signal and then was fit by mono and double exponential function to obtain decorrelation time. These translational decorrelation times corresponding to translational diffusion coefficients enabled us to find the controlled vis cosity of the medium. The viscosities of the media were compared with literature values and a fair/excellent agreement was observed. Thus, the technique has ability to quantify the salt levels in terms of viscosity in non flowing medium suspensions and many research routes necessary to determine its potential for in vivo appli cations.

We report a new method for glucose monitoring in blood tissue based on the autocorrelation function (ACF) analysis in Fourier domain optical coherence tomography (FD-OCT). We have determined the changes in OCT monitoring signals' depth to characterize the modulations in ACFs for quantitative measurements of glucose concentrations in blood. We found that an increase in the concentration of glucose in blood results in decreased OCT monitoring signal due to the increase in the refractive index of the media.

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The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain‐related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas‐Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.