Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation.

This paper comprehensively reviews two major image processing tasks, such as restoration and segmentation in the medical field, from a deep learning perspective. These processes are essential because restoration removes noise and segmentation extracts the specific region of interest of an image, both of which are necessary for accurate diagnosis and therapy. This paper mainly focuses on deep learning techniques. It plays a prominent role over other conventional techniques in handling a large number of datasets in the medical field and provides accurate results. This paper reviewed the application of different convolutional neural network architectures in the restoration and segmentation processes. Based on the results in the case of image restoration, TLR-CNN and Stat-CNN are promising in achieving better PSNR, noise suppression, artifact suppression and improving the overall image quality. For the segmentation process, LCP net achieves the Dice score of 98.12% and sensitivity of 98.95% in the cell contour segmentation; the 3D FCNN model is found to be the best method for the segmentation of brain tumors. This review shows that deep learning methodologies can be a better alternative for medical image restoration and segmentation tasks, as data size is an important concern today.

Preparation and characterization of hybrid chitosan-silver nanoparticles (Chi-Ag NPs); A potential antibacterial agent.

In this study, a novel ecofriendly chitosan- silver nanoparticles hybrid was developed. Biological method using leaf extract of T. portulacifolium was used as reducing agent for its synthesis and the antibacterial efficiency of these hybrid nanoparticles were evaluated against the bacteria E. coli and S. marcescens organisms. The intense peak observed around 419 nm in the UV-Vis indicates the formation of silver nanoparticles. The XRD analysis showed that the hybrid chitosan-silver nanoparticles have a polycrystalline and face-centered cubic configuration. FTIR spectrum hybrid chitosan-silver nanoparticles indicated speaks vibration of NH and OH. The EDS analysis confirmed the presence of Ag, O, C and N elements in the prepared sample. The spherical shape was obtained from TEM analysis and it indicated that with average particles around 3.24 nm to 44.80 nm. The prepared hybrid chitosan-silver nanoparticles showed significant antibacterial activities against E. coli and S. marcescens. In addition, the surface membrane damages and surface morphology of test pathogens were visualized using FESEM analysis.