Smartphone-based imaging devices (SIDs) have shown to be versatile and have a wide range of biomedical applications. With the increasing demand for high-quality medical services, technological interventions such as portable devices that can be used in remote and resource-less conditions and have an impact on quantity and quality of care. Additionally, smartphone-based devices have shown their application in the field of teleimaging, food technology, education, etc. Depending on the application and imaging capability required, the optical arrangement of the SID varies which enables them to be used in multiple setups like bright-field, fluorescence, dark-field, and multiple arrays with certain changes in their optics and illumination. This comprehensive review discusses the numerous applications and development of SIDs towards histopathological examination, detection of bacteria and viruses, food technology, and routine diagnosis. Smartphone-based devices are complemented with deep learning methods to further increase the efficiency of the devices.

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Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places. Graphical abstract

Severe Acute Respiratory Syndrome Coronaviruses (SARS-CoVs), causative of major outbreaks in the past two decades, has claimed many lives all over the world. The virus effectively spreads through saliva aerosols or nasal discharge from an infected person. Currently, no specific vaccines or treatments exist for coronavirus; however, several attempts are being made to develop possible treatments. Hence, it is important to study the viral structure and life cycle to understand its functionality, activity, and infectious nature. Further, such studies can aid in the development of vaccinations against this virus. Microscopy plays an important role in examining the structure and topology of the virus as well as pathogenesis in infected host cells. This review deals with different microscopy techniques including electron microscopy, atomic force microscopy, fluorescence microscopy as well as computational methods to elucidate various prospects of this life-threatening virus. Highlights • Structural analysis of SARS-CoVs aids in understanding its nature, activity, and pathophysiology • Revealing the surface morphology of SARS-CoVs using scanning electron microscope and atomic force microscopy • Computational methods help to understand the structure of SARS-CoVs and their

The microstructural analysis of tissues plays a crucial role in the early detection of abnormal tissue morphology. Polarization microscopy, an optical tool for studying the anisotropic properties of biomolecules, can distinguish normal and malignant tissue features even in the absence of exogenous labelling. To facilitate the quantitative analysis, we developed a polarization-sensitive label-free imaging system based on the Stokes-Mueller calculus. Polarization images of ductal carcinoma tissue samples were obtained using various input polarization states and Stokes-Mueller images were reconstructed using Matlab software. Further, polarization properties, such as degree of linear and circular polarization and anisotropy, were reconstructed from the Stokes images. The Mueller matrix obtained was decomposed using the Lu-Chipman decomposition method to acquire the individual polarization properties of the sample, such as depolarization, diattenuation and retardance. By using the statistical parameters obtained from the polarization images, a support vector machine (SVM) algorithm was trained to facilitate the tissue classification associated with its pathological condition.