Fiber endoscopes have emerged as a vital tool for highresolution Recent advancements have adopted deep learning techniques microscopic imaging in hard-to-reach areas. In contrast to expedite the QPI image reconstruction process [12, 13]. Moreover, to conventional endoscopes with a typical diameter of extant literature indicates the potential of decrypting an several millimeters, fiber endoscopes, which could be submillimeter encoded phase directly from speckle images utilizing deep learning, thin and flexible [1-5], can pass through the organ's although only in simulated environments [14]. This demonstrates intricate pathways without causing harm inside the body [6], the theoretical possibility of reconstructing the original making them particularly suitable for procedures requiring utmost phase directly from speckle images using deep learning for MCF precision and minimal invasiveness. The reduced size and phase imaging, however, networks trained on simulated data adaptability of fiber endoscopes ensure less discomfort for the can hardly achieve accurate phase reconstructions in real-world patient, leading to quicker recovery times and a lower risk of optical systems.

The global AI-based fever detection camera market size is USD1.28 billion by 2020 and is projected to reach USD 2.19 billion by 2027, exhibiting a CAGR of 8.0% during the forecast period. The worldwide surge in the growth of corona infected people has led to the emergence of advanced artificial intelligence-based fever detection cameras to monitor and detect human body temperature. Vaccine for coronavirus is still in its development stage and hence, the only way to reduce the spread of this pandemic is to isolate the infected person from the crowd. This type of camera is being considered as an efficient and effective device to identify a person with high temperature as fever is one of the symptoms of coronavirus. An individual with high temperature is further screened with virus-specific tests.