The 4th African Conference on Fundamental and Applied Physics (ACP2025)

Direct detection and classification of E. coli and fecal streptococci using an optical water droplet method and convolutional neural networks

16 Sept 2025, 16:25

5m

Contributed Poster Presentation Physics Research Poster Room

Speaker

Lizette Nange CHIA (University of Dschang, Cameroon)

Description

Detecting harmful bacteria in drinking water is a significant concern for public health. Indicator bacteria like E.
coli and fecal streptococci serve as markers for fecal contamination of water [Environmental and Pollution
Science (2019), pp. 191]. Several detection methods of these markers require costly equipment, and
specialized laboratories and technicians, and are time-consuming, resulting in labor-intensive processes [J.
Phys. Conf. Ser. 995, 012065 (2018)]. This paper proposes an optimal method that combines an optical water
droplet method and convolutional neural networks (CNNs) to provide an accurate and cost- and time-effective
approach to directly detect and classify E. coli and fecal streptococci in water. The system captures images of
indicator bacteria and then classifies them. We obtain a classification accuracy of up to 0.89 and a loss of
0.13. This work constitutes a step toward an integrated, real-time, and automatic optical-based detection
system for water-borne pathogenic agents.

Presentation materials

Lizette_Nange_CHIA_Presentation - Chia Lizette Nange.pdf