Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater

Identifying the microbial population and type of them is a crucial measure in water wastewater treatment processes, reuse wastewater, sludge system. Todays, manual methods are usually used to count detect bacteria sewage laboratories which mostly suffer from human errors. This study aims at presenting an accurate method based on image analysis through convolution neural network (CNN) classify Escherichia coli (E. coli) Vibrio cholera (V. cholera) bacteria, wastewater. About 9,000 Red-Green-Blue (RGB) microscopic images sample containing stained were as input datasets. The results showed that would be classified counted with accuracy 93.01% 97.0%, respectively. While CNN performed pretty well counting number for both RGB grayscale color models, its classification performance only satisfactory images. sensitivity illustrated Gaussian noise enhancement caused increment standard deviation () proportionally decreased accuracy.