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Erythrocytes

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Diagnosis of Diabetes Mellitus by Extraction of Morphological Features of Red Blood Cells Using an Artificial Neural Network.

Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association
Diabetes mellitus is a metabolic disorder characterized by varying hyperglycemias either due to insufficient secretion of insulin by the pancreas or improper utilization of glucose. The study was aimed to investigate the association of morphological...

Healthy and unhealthy red blood cell detection in human blood smears using neural networks.

Micron (Oxford, England : 1993)
One of the most common diseases that affect human red blood cells (RBCs) is anaemia. To diagnose anaemia, the following methods are typically employed: an identification process that is based on measuring the level of haemoglobin and the classificati...

Automatic Identification of Human Erythrocytes in Microscopic Fecal Specimens.

Journal of medical systems
Traditional fecal erythrocyte detection is performed via a manual operation that is unsuitable because it depends significantly on the expertise of individual inspectors. To recognize human erythrocytes automatically and precisely, automatic segmenta...

Cheminformatics Based Machine Learning Models for AMA1-RON2 Abrogators for Inhibiting Plasmodium falciparum Erythrocyte Invasion.

Molecular informatics
Malaria remains a dreadful disease by putting every year about 3.4 billion people at risk and resulting into mortality of 627 thousand people worldwide. Existing therapies based upon Quinines and Artemisinin-based combination therapies have started s...

Artificial Neural Network-Aided Computational Approach for Mechanophenotyping of Biological Cells Using Atomic Force Microscopy.

Journal of biomechanical engineering
The artificial neural network (ANN) based models have shown the potential to provide alternate data-driven solutions in disease diagnostics, cell sorting and overcoming AFM-related limitations. Hertzian model-based prediction of mechanical properties...

An automated malaria cells detection from thin blood smear images using deep learning.

Tropical biomedicine
Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effect...

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network.

Archives of pathology & laboratory medicine
CONTEXT.—: The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges.

Increasing a microscope's effective field of view via overlapped imaging and machine learning.

Optics express
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morpholog...

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

Optics letters
Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are...

Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling m...