Related the physio-pathological process of covid-19 disease it is interesting to focus to the aspect played by interaction of SARS-COV-2 protein with integrins of human epithelial pulmonary cell. A bio molecular approach help in to deeply verify the involved factors and the results of this activation RGD mediated of Great interest also the relationship with some vaccine strategy followed by the various pharmaceutical industry. The results of this work will be useful to think modification in some vaccine increasing the global safety and related some rare ADR.
Irad Mwendo*, Patrick Gikunda and Anthony Maina
DOI: 10.37421/2472-1018.2023.9.180
Chest X-rays remains to be the most common imaging modality used to diagnose lung diseases. However, they necessitate the interpretation of experts (radiologists and pulmonologists), who are few. This review paper investigates the use of deep transfer learning techniques to detect COVID-19, pneumonia and tuberculosis in Chest X-Ray (CXR) images. It provides an overview of current state of the art CXR image classification techniques and discusses the challenges and opportunities in applying transfer learning to this domain. The paper provides a thorough examination of recent research studies that used deep transfer learning algorithms for COVID-19, pneumonia and tuberculosis detection, highlighting the advantages and disadvantages of these approaches. Finally, the review paper discusses future research directions in the field of deep transfer learning for CXR image classification, as well as the potential for these techniques to aid in the diagnosis and treatment of lung diseases.
Journal of Lung Diseases & Treatment received 247 citations as per Google Scholar report
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