Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images.

Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vision. The COVID-19 detection is the subject of our medical study. Researchers attempted to increase the detection accuracy but at the cost of high model complexity. In this paper, we desire to achieve better accuracy with little training space and time so that this model easily deployed in edge devices. In this paper, a new CNN design is proposed that has three stages: pre-processing, which removes the black padding on the side initially; convolution, which employs filter banks; and feature extraction, which makes use of deep convolutional layers with skip connections. In order to train the model, chest X-ray images are partitioned into three sets: learning(0.7), validation(0.1), and testing(0.2). The models are then evaluated using the test and training data. The LMNet, CoroNet, CVDNet, and Deep GRU-CNN models are the other four models used in the same experiment. The propose model achieved 99.47% & 98.91% accuracy on training and testing respectively. Additionally, it achieved 97.54%, 98.19%, 99.49%, and 97.86% scores for precision, recall, specificity, and f1-score respectively. The proposed model obtained nearly equivalent accuracy and other similar metrics when compared with other models but greatly reduced the model complexity. Moreover, it is found that proposed model is less prone to over fitting as compared to other models.

Comparative transcriptome profiling of Polianthes tuberosa during a compatible interaction with root-knot nematode Meloidogyne incognita.

BACKGROUND: The root-knot nematode (RKN; Meloidogyne spp.) is the most destructive plant parasitic nematode known to date. RKN infections, especially those caused by Meloidogyne incognita, are one of the most serious diseases of tuberose. METHODS AND RESULTS: To investigate the molecular mechanism in the host-pathogen interactions, the Illumina sequencing platform was employed to generate comparative transcriptome profiles of uninfected and Meloidogyne incognita-infected tuberose plants, during early, mid, and late infection stage. A total of 7.5 GB (49 million reads) and 9.3 GB (61 million reads) of high-quality data was generated for the control and infected samples, respectively. These reads were combined and assembled using the Trinity assembly program which clustered them into 1,25,060 unigenes. A total of 85,360 validated CDS were obtained from the combined transcriptome whereas 6,795 CDS and 7,778 CDS were found in the data for the control and infected samples, respectively. Gene ontology terms were assigned to 958 and 1,310 CDSs from the control and infected data, respectively. The KAAS pathway analysis revealed that 1,248 CDS in the control sample and 1,482 CDS in the infected sample were enriched with KEGG pathways. The major proportions of CDS were annotated for carbohydrate metabolism, signal transduction and translation related pathways in control and infected samples. Of the 8,289 CDS commonly expressed between the control and infected plants, 256 were significantly upregulated and 129 were significantly downregulated in the infected plants. CONCLUSIONS: Collectively, our results provide a comprehensive gene expression changes in tuberose during its association with RKNs and point to candidate genes that are involved in nematode stress signaling for further investigation. This is the first report addressing genes associated with M. incognita-tuberose interaction and the results have important implications for further characterization of RKN resistance genes in tuberose.