Automated Microbial Classification System based on Deep Convolutional Neural Networks using Images from Colony Picker

Colony screening in single and multi-species environments is an essential step for microbiome studies. However, it possesses a high possibility of inaccurately classifying the species of interest and demands a high degree of manpower and time. Thus, automating the classification of microbes is beneficial to minimize the time and inaccuracy in the colony screening/picking step. Here, we developed an automated microbial classification system for five target species, based on deep Convolutional Neural Networks (CNN) using images captured by an automated robotic colony picker. Multiple possible scenarios of colony culture and diverse morphologies of colonies were examined in building the training and test datasets to train and validate the model and performance on real-life implementations. The final model trained using 60,000 training images, with 12,000 images per species and 3-fold cross-validation, achieved a test accuracy of 94.2% and a test loss of 0.154. Upon testing using a deployment dataset of 4,500 images (900 images per species) with different methods of applying cells onto the agar plate, high accuracy of up to 96.6% was obtained. Five evaluation metrics were implemented to evaluate diverse scenarios of the test data to justify the validity of the model in real-life applications. This model forms a foundation for the classification of more species through transfer learning in the future.