Detection and analysis of COVID-19 in medical images using deep learning techniques

The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.The research leading to these results received funding from the Innovative Medicines Innitiative 2 Joint Undertaking (JU) under grant agreement No 853989. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Global Alliance for TB Drug Development non profit organisation, Bill & Melinda Gates Foundation and University of Dundee

Ecological Unconscious, Animals and Psychological Trauma in Monique Roffey’s Archipelago Diren Ashok

Monique Roffey is a Trinidadian-born award winning writer who has produced a number of famous novels and a memoir. In her recent novel, Archipelago (2012), issues on redemption, loss of hope and healing were highlighted in the wake of a devastating natural disaster that swept across the Caribbean Island of Trinidad. Life was a complete change for the chief protagonist, Gavin Weald, as the catastrophic flood not only destroyed his home but also put great psychological strains which affected him and his family. In order to combat the distressing ordeal, Gavin and his daughter- alongside with their dog- decided to set sail and to make peace with the very ocean that caused the misfortune upon them. This research aims to validate the authenticity and importance nature plays in overcoming trauma that has been caused by the flood. In order to carry out this research, the concepts of ecological unconscious and dualism under the lenses of Eco-psychology by Theodore Roszak and Andy Fisher as well as trauma by Cathy Caruth will be employed in analysing how nature plays a pertinent role in healing trauma caused by the floods in this novel. This study aspires to explicate further the relationship between human and animals and how this union helps to overcome psychological disturbances experienced by the characters