ABSTRACT
Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.
ABSTRACT
In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.
ABSTRACT
The entire world is now agog with the novel coronavirus disease-2019 (COVID-19) chaos, as it has caused unexpected devastation in almost every country. As the resurgence of the COVID-19 is most certain in the coming two years (most probably during the winter season), extremely stringent methods have to be practiced to lower its spreading. The appropriate measures to be followed by wearing a mask (both infected as well as healthy population), adopting social distancing methods, regular medical checkups, and lowering of the grim emissions of toxins from heavy industries around the world, and other effective methods. This study has presented databases of COVID-19 (as of 25 May 2020), which revealed that most of the western countries are showing decreasing trends after being attained the maximum peak of the ‘Gaussian shape’. In contrast, Asian countries (India, Indonesia, and Pakistan) show, alarmingly, increasing trends that imply great care has to be taken to fight against COVID-19. It is well known that nanomaterials have unique physical and chemical properties (ultra-small size, large surface-area-to-mass ratio, and high reactivity) that are different from bulk materials of the same composition. Therefore, this paper discusses the role of nanomaterials in identifying and offering protection against the virus. Also, it gives an insight into their use in the treatment to treat COVID infected patients. Furthermore, this paper also elaborated the challenges being faced while dealing with coronaviruses with a few feasible solutions to eradicate this untoward danger of the pandemic. A meaningful schematic on ‘flattening of the curve’ is included in this paper by considering several factors into account that may help to lessen or eradicate the present pandemic. © (2020) Society for Biomaterials & Artificial Organs #20042020