Classification and Detection of Pathogens from Enhanced Microscopic Images


  • Isra Naz COMSATS University islamabad wah campus
  • Muhammad Abdullah


Drinking water is essential for human life but unfortunately every year, many lives are lost as a result of the use of polluted water. Computerized methods play a dynamic role in detecting pathogens from water. The first symptoms of pathogens in water are difficult to detect by the naked eye at an early stage. Therefore, in this research, a computerized method is proposed in which features are extracted from the pre-trained ResNet-50 model, and classification of the different types of pathogens is performed using the SoftMax layer. The proposed method's performance is evaluated on the proposed microscopic pathogen dataset. The proposed dataset is pre-processed using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) method for the enhancement of Image quality. The proposed method provides greater than 90% prediction accuracy.


S. Sharma, and A. Bhattacharya, “Drinking water contamination and treatment techniques,” Applied water science, vol. 7, no. 3, pp. 1043-1067, 2017.

J. P. Cabral, “Water microbiology. Bacterial pathogens and water,” International journal of environmental research and public health, vol. 7, no. 10, pp. 3657-3703, 2010.

F. J. Loge, D. E. Thompson, and D. R. Call, “PCR detection of specific pathogens in water: a risk-based analysis,” Environmental science & technology, vol. 36, no. 12, pp. 2754-2759, 2002.

A. M. Fayyaz, K. A. Al-Dhlan, S. U. Rehman, M. Raza, W. Mehmood, M. Shafiq, and J.-G. Choi, “Leaf Blights Detection and Classification in Large Scale Applications,” INTELLIGENT AUTOMATION AND SOFT COMPUTING, vol. 31, no. 1, pp. 507-522, 2022.

S. N. Zulkifli, H. A. Rahim, and W.-J. Lau, “Detection of contaminants in water supply: A review on state-of-the-art monitoring technologies and their applications,” Sensors and Actuators B: Chemical, vol. 255, pp. 2657-2689, 2018.

T. IRANİ, H. AMİRİ, S. AZADİ, M. BAYAT, and H. DEYHİM, “Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater,” Environmental Research and Technology, vol. 5, no. 1, pp. 101-110, 2022.

J. Selinummi, J. Seppälä, O. Yli-Harja, and J. A. Puhakka, “Software for quantification of labeled bacteria from digital microscope images by automated image analysis,” Biotechniques, vol. 39, no. 6, pp. 859-863, 2005.

N. Blackburn, Å. Hagström, J. Wikner, R. Cuadros-Hansson, and P. K. Bjørnsen, “Rapid determination of bacterial abundance, biovolume, morphology, and growth by neural network-based image analysis,” Applied and Environmental Microbiology, vol. 64, no. 9, pp. 3246-3255, 1998.

C. A. Osunla, and A. I. Okoh, “Vibrio pathogens: A public health concern in rural water resources in sub-Saharan Africa,” International journal of environmental research and public health, vol. 14, no. 10, pp. 1188, 2017.

N. W. Schaad, E. Postnikova, G. Lacy, A. Sechler, I. V. Agarkova, P. E. Stromberg, V. K. Stromberg, and A. M. Vidaver, “Emended classification of xanthomonad pathogens on citrus,” Papers in Plant Pathology, pp. 96, 2006.

A. K. Tamrakar, M. Jain, A. K. Goel, D. V. Kamboj, and L. Singh, “Characterization of Vibrio cholerae from deep ground water in a cholera endemic area in Central India,” Indian journal of microbiology, vol. 49, no. 3, pp. 271-275, 2009.

T. G. Aw, and J. B. Rose, “Detection of pathogens in water: from phylochips to qPCR to pyrosequencing,” Current opinion in biotechnology, vol. 23, no. 3, pp. 422-430, 2012.

S. Toze, “PCR and the detection of microbial pathogens in water and wastewater,” Water Research, vol. 33, no. 17, pp. 3545-3556, 1999.

N. Sieber, H. Hartikainen, and C. Vorburger, “Validation of an eDNA-based method for the detection of wildlife pathogens in water,” Diseases of Aquatic Organisms, vol. 141, pp. 171-184, 2020.

A. Bosch, S. Guix, D. Sano, and R. M. Pinto, “New tools for the study and direct surveillance of viral pathogens in water,” Current Opinion in Biotechnology, vol. 19, no. 3, pp. 295-301, 2008.

R. Girones, M. A. Ferrus, J. L. Alonso, J. Rodriguez-Manzano, B. Calgua, A. de Abreu Correˆa, A. Hundesa, A. Carratala, and S. Bofill-Mas, “Molecular detection of pathogens in water–the pros and cons of molecular techniques,” Water research, vol. 44, no. 15, pp. 4325-4339, 2010.

A. Muiz Fayyaz, M. Kolivand, J. Alyami, S. Roy, and A. Rehman, "Computer Vision-Based Prognostic Modelling of COVID-19 from Medical Imaging," Prognostic Models in Healthcare: AI and Statistical Approaches, pp. 25-45: Springer, 2022.

E. A. Hay, and R. Parthasarathy, “Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets,” PLoS computational biology, vol. 14, no. 12, pp. e1006628, 2018.

M. F. Wahid, M. J. Hasan, M. S. Alom, and S. Mahbub, "Performance Analysis of Machine Learning Techniques for Microscopic Bacteria Image Classification." pp. 1-4.

B. D. Satoto, I. Utoyo, R. Rulaningtyas, and E. B. Khoendori, “An improvement of Gram-negative bacteria identification using convolutional neural network with fine tuning,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 18, no. 3, pp. 1397-1405, 2020.

F. Hoorali, H. Khosravi, and B. Moradi, “Automatic Bacillus anthracis bacteria detection and segmentation in microscopic images using UNet++,” Journal of Microbiological Methods, vol. 177, pp. 106056, 2020.

Y. Seo, B. Park, S.-C. Yoon, K. C. Lawrence, and G. R. Gamble, “Morphological image analysis for foodborne bacteria classification,” Transactions of the ASABE, vol. 61, no. 1, pp. 5-13, 2018.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, and Z. Wang, "Photo-realistic single image super-resolution using a generative adversarial network." pp. 4681-4690.

A. S. B. Reddy, and D. S. Juliet, "Transfer learning with ResNet-50 for malaria cell-image classification." pp. 0945-0949.




How to Cite

Isra Naz, & Muhammad Abdullah. (2023). Classification and Detection of Pathogens from Enhanced Microscopic Images. University of Wah Journal of Computer Science, 4(1), 8–16. Retrieved from