Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread.

Effective strategies to restrain COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing a mask is among the non-pharmaceutical intervention measures that can be used to cut the primary source of SARS-CoV2 droplets expelled by an infected individual. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over the nose and mouth in public. To contribute towards communal health, this paper aims to devise a highly accurate and real-time technique that can efficiently detect non-mask faces in public and thus, enforcing to wear mask. The proposed technique is ensemble of one-stage and two-stage detectors to achieve low inference time and high accuracy. We start with ResNet50 as a baseline and applied the concept of transfer learning to fuse high-level semantic information in multiple feature maps. In addition, we also propose a bounding box transformation to improve localization performance during mask detection. The experiment is conducted with three popular baseline models viz. ResNet50, AlexNet and MobileNet. We explored the possibility of these models to plug-in with the proposed model so that highly accurate results can be achieved in less inference time. It is observed that the proposed technique achieves high accuracy (98.2%) when implemented with ResNet50. Besides, the proposed model generates 11.07% and 6.44% higher precision and recall in mask detection when compared to the recent public baseline model published as RetinaFaceMask detector. The outstanding performance of the proposed model is highly suitable for video surveillance devices.

Pioglitazone hydrochloride: chemopreventive potential and development of site-specific drug delivery systems.

The aim of this study was to investigate the potential of pioglitazone hydrochloride as a promising anticancer agent and then to design and evaluate the colon-targeted delivery system. The role of pioglitazone hydrochloride as a promising anticancer agent was evaluated by in vitro cell line studies and in vivo 1,2-dimethylhydrazine-induced colon carcinogenesis in rats. In order to deliver the drug at site of action, i.e. colon, drug embedded in matrices containing a release retarding polymer (HPMC K4M) and a polysaccharide (locust bean gum) were prepared. These matrix systems were further enteric coated with Eudragit®S100 to minimize the premature drug release in the upper segments of the GIT. In vitro dissolution studies were performed in absence and presence of rat caecal contents on selected batches and samples were analyzed using a validated RP-HPLC method. Hence, the studies led to the conclusion that successful site-specific delivery systems of pioglitazone hydrochloride were developed to improve its therapeutic efficacy in the management of colorectal cancer.