Classification and Detection of Pathogens from Enhanced Microscopic Images
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.
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