Covid 19-Lung Segmentation and Detection-A Literature Review

The 2019 corona virus pandemic (COVID-19) has expanded worldwide. Medical imaging, such as X-rays and CT, plays a crucial part in the worldwide fight of COVID-19, whilst new technologies of artificial intelligence (AI) further increase the imagery tools and assist medical professionals. We examine the quick reactions to COVID-19 in the medical imaging community, propelled by AI. For example, AI-enhanced picture collection may greatly assist to automate the process of scanning and also restructure the workflow with little patient interaction, giving the imagery professionals the greatest protection. In this review, the methods of extracting the features for lung CT images and for segmentation and classification we searched many data source like IEEE, Elsevier, Springer, the correct delineation of infectious X-ray and CT images by AI may further increase the job efficiency, making quantification afterwards more efficient. In addition, radiographers make clinical judgments, for example for diagnosis, tracking and prognosis of the disease, with their computer assist platforms. This review study thus covers the complete medical imagers' pipeline, including the capture of images, segmentation, diagnosis, and follow-up approaches including COVID-19. The implementation of Smart into X-ray and CT, both frequently employed in frontline hospitals, is particularly important to show the newest advances in the fight against COVID-19 in diagnostic imaging and radiology. X-rays and CT in chests are commonly employed in the COVID-19 testing and diagnosis. In order to minimise the high danger of infection during the COVID-19 pandemic, contactless and automated image capture workflows are needed. The usual process of imagery however, entails inescapable interaction between technicians and patients. In particular, the technicians assist in the positioning of the patient first in posing the patient according to a certain protocol, such as first-head versus first-foot, and supine versus prone at CT, then visually identify the target part of the patient's body location, and manually adjust the relative position and position between the patient and the x- ray tube. This procedure enables personnel to touch patients closely, which leads to significant risk of virus exposure. A contactless and automated picture process is therefore necessary to reduce interaction.

Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation.

The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this virus, diagnosis and prognosis of the infections. Though Chest CT and Chest X-ray imaging are common in the clinical protocols of COVID-19 management, the latter is highly preferred, attributed to its simple image acquisition procedure and mobility of the imaging mechanism. However, Chest X-ray images are found to be less sensitive compared to Chest CT images in detecting infections in the early stages. In this paper, we propose a deep learning based framework to enhance the diagnostic values of these images for improved clinical outcomes. It is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities, which is trained with deep features extracted from the Chest X-ray images of a standard dataset for binary and multi class classification. The binary classifier achieves an accuracy of 99.53% in the discrimination of COVID-19 and Non COVID-19 images. Similarly, the multi class classifier performs classification of COVID-19, Viral Pneumonia and Normal cases with an accuracy of 99.79%. This model called the COVID-19 Super pixel SqueezNet (COVID-SSNet) performs super pixel segmentation of the activation maps to extract the regions of interest which carry perceptual image features and constructs an overlay of the Chest X-ray images with these regions. The proposed classifier model adds significant value to the Chest X-rays for an integral examination of the image features and the image regions influencing the classifier decisions to expedite the COVID-19 treatment regimen.

Breathe analyser for respiratory diseases: A review

In present scenario respiratory diseases are major challenge in healthcare sector and many individuals suffer from respiratory diseases like asthma, lung cancer, throat cancer etc., due to air pollution, smoking and infections in respiratory system. Sometimes the respiratory diseases will become pandemic diseases such as SARS and COVID-19. In order to protect people from these pandemic diseases, detecting the diseases at earlier stage is much essential by proper diagnosis. The two diagnostic techniques are the invasive and non-invasive techniques. Diagnostic equipment like Computed Tomography (CT), Biomarkers and Artificial Electronic Nose are generally used in hospitals, however these methods are not affordable for common people. This paper reviews various invasive and non-invasive techniques and also tries to analyze cost effective device for diagnosing respiratory diseases. © 2019 SERSC.