ABSTRACT
Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.
Subject(s)
Holography , Lenses , Microscopy/methods , Lighting , Holography/methods , AlgorithmsABSTRACT
Cervical cancer death rates have been increasing during the last five years in the world, especially in Brazil and Latin America. One of the reasons for this phenomenon is the stalling of preventive cervical cancer screening tests, due to the lack of specialists with proper technologies for massive screening. The COVID-19 pandemic has worsened the situation as several Pap smearing tests have been postponed. The goal of this work is to present the design of a portable, low-cost device for digital screen test analysis to support cervical cancer detection in low-income and remote areas. We were able to design and test a low-cost, easy to implement device for conventional Pap smearing with high accuracy to detect anomalies in cervical cells. The total cost of the device is lower than 400 dollars. © 2022 IEEE.
ABSTRACT
COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore, confirmed by the swab and Chest X-Ray methods. The process of swab and Chest X-Ray takes a relatively long time since in Chest X-Ray some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with Discrete Wavelet Transform as feature extraction techniques and deep learning as the classification method. The result of this study capable to identify Chest X-Ray with COVID-19 and the accuracy increased of more than 10% on Support Vector Machine, Decision Tree and Deep Learning. So that, the comparison result showed that feature extraction was able to significantly improve accuracy. © 2021 IEEE.
ABSTRACT
The vein-viewer is a new revolution in the health industry. In fact, it is one of the must-have gadgets for any medical professional. The vein-viewer is device that helps to access easily veins when trying to collect a blood sample or for administering Intravenous (IV) cannulation. It is also an aid for dermatologist/aesthetic physician to access client's veins for sclerotherapy procedures or avoiding veins in cosmetic procedures. The vein-viewer is highly applicable where vascular positioning is really difficult; examples while canulating infants, obese, hairy/dark skins, dialysis/cancer patients etc. In addition, frequent attempts affect patients, causing trauma and subcutaneous haemorrhage. As palm/finger vein patterns are unique and complex, difficult to duplicate or steal as it is beneath the skin. So, in this Covid19 pandemic time, the vein-viewer finds applications in the secure non-contact bio-metric authentications for secure banking and attendance registering system to identify an individual. In this article I am trying to explain the design overview of vein-viewer system, its design challenges, cost aspects, its availability and also sharing a few inputs for the new compact, low-cost design and implementation.