Classification of Bacterial Species from Microbiological Photographic Images

Classifying bacteria species using traditional methods can be both costly and time-consuming due to the hard and rigorous process of biomedical tests. Using machine learning, an automated bacteria species classification system can provide an efficient and cost-effective solution. The goal of this research is to deliver an innovative bacterial species detection technique using machine learning that can accurately classify species of bacteria from microbiological photographic images which will be helpful for researchers and clinicians working in this field. DIBaS dataset (Digital Image of Bacterial Species) used for the study contains images of 33 species with a total of 681 images. This research was done using a modified version of the VGG16 pre-trained neural network with 94.49% accuracy. Finally, the model was compared using VGG19 and DenseNet-121 pre-trained models with the same configuration.