The present paper introduces a focus stacking‐based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2‐level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand‐engineered features. The slide images are acquired with a custom‐built portable slide scanner made from low‐cost, off‐the‐shelf components and is suitable for point‐of‐care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.
Escherichia coli (E. coli) bacteria have been identified to be the cause of variety of health outbreaks resulting from contamination of food and water. Timely and rapid detection of the bacteria is thus crucial to maintain desired quality of food products and water resources. A novel methodology proposed in this paper demonstrates for the first time, the feasibility of employing a bare fiber Bragg grating (bFBG) sensor for detection of E. coli bacteria. The sensor was fabricated in a photo-sensitive optical fiber (4.2 µm/80 µm). Anti-E. coli antibody was immobilized on the sensor surface to enable the capture of target cells/bacteria present in the sample solution. Strain induced on the sensor surface as a result of antibody immobilization and subsequent binding of E. coli bacteria resulted in unique wavelength shifts in the respective recording of the reflected Bragg wavelength, which can be exploited for the application of biosensing. Functionalization and antibody binding on to the fiber surface was cross validated by the color development resulting from the reaction of an appropriate substrate solution with the enzyme label conjugated to the anti-E. coli antibody. Scanning electron microscope image of the fiber, further verified the E. coli cells bound to the antibody immobilized sensor surface.
Cost-effective and automated acquisition of whole slide images is a bottleneck for wide-scale deployment of digital pathology. In this article, a computation augmented approach for the development of an automated microscope slide scanner is presented. The realization of a prototype device built using inexpensive off-the-shelf optical components and motors is detailed. The applicability of the developed prototype to clinical diagnostic testing is demonstrated by generating good quality digital images of malaria-infected blood smears. Further, the acquired slide images have been processed to identify and count the number of malaria-infected red blood cells and thereby perform quantitative parasitemia level estimation. The presented prototype would enable cost-effective deployment of slide-based cyto-diagnostic testing in endemic areas.
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