Examination of blood samples using deep learning and mobile microscopy
- Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.
Author: | Juliane PfeilORCiD, Alina NechyporenkoORCiD, Marcus FrohmeORCiDGND, Frank T. Hufert, Katja Schulze |
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URN: | urn:nbn:de:kobv:526-opus4-15802 |
DOI (Version of Record): | https://doi.org/10.1186/s12859-022-04602-4 |
ISSN: | 1471-2105 |
Parent Title (English): | BMC Bioinformatics |
Publisher: | BioMed Central |
Document Type: | Article |
Language: | English |
Year of Publication: | 2022 |
Publishing Institution: | Technische Hochschule Wildau |
Release Date: | 2022/02/14 |
Volume: | 23 |
Article Number: | 65 |
Source: | Pfeil, J., Nechyporenko, A., Frohme, M. et al. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics 23, 65 (2022). https://doi.org/10.1186/s12859-022-04602-4 |
Faculties an central facilities: | Fachbereich Ingenieur- und Naturwissenschaften |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Funding: | Projekt DEAL |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |