Classification of E. coli Colony with Generative Adversarial Networks, Discrete Wavelet Transforms and VGG19

Author(s): Pappu Kumar Yadav, Thomas Burks, Kunal Dudhe , Quentin Frederick, Jianwei Qin, Moon Kim, and Mark A. Ritenour

The transmission of Escherichia coli (E. coli) bacteria to humans through infected fruits, such as citrus, can lead to severe health issues, including bloody diarrhea and kidney disease (Hemolytic Uremic Syndrome). Therefore, the implementation of a suitable sensor and detection approach for inspecting the presence of E. coli colonies on fruits and vegetables would greatly enhance food safety measures. This journal article presents an evaluation of SafetySpect's Contamination, Sanitization Inspection, and Disinfection (CSI-D+) system, comprising an UV camera, an RGB camera, and illumination at two fluorescence excitation wavelengths: ultraviolet C (UVC) at 275 nm and violet at 405 nm. To conduct the study, different concentrations of bacterial populations were inoculated on black rubber slides, chosen to provide a fluorescence-free background for benchmark tests on E. coli-containing droplets. A VGG19 deep learning network was used for classifying fluorescence images with E. coli droplets at four concentration levels. Discrete wavelet transforms (DWT) were used to denoise the images and then generative adversarial networks (StyleGAN2-ADA) were used to enhance dataset size to mitigate the issue of overfitting. It was found that VGG19 with SoftMax achieved an overall accuracy of 84% without synthetic datasets and 94% with augmented datasets generated by StyleGAN2-ADA. Furthermore, employing RBF SVM increased the accuracy by 2% points to 96%, while Linear SVM enhanced it by 3% points to 97%. These findings provide valuable insights for the detection of E. coli bacterial populations on citrus peels, facilitating necessary actions for decontamination

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