Deep Learning Approach for Classifying Bacteria types using Morphology of Bacterial Colony.

The significant bottlenecks in determining bacterial species are much more time-consuming and the biology specialist's long-term experience requirements. Specifically, it takes more than half a day to cultivate a bacterium, and then a skilled microbiologist and a costly specialized machine are utilized to analyze the genes and classify the bacterium according to its nucleotide sequence. To overcome these issues as well as get higher recognition accuracy, we proposed applying convolutional neural networks (CNNs) architectures to automatically classify bacterial species based on some key characteristics of bacterial colonies. Our experiment confirmed that the classification of three bacterial colonies could be performed with the highest accuracy (97.19%) using a training set of 5000 augmented images derived from the 40 original photos taken in the Hanoi Medical University laboratory in Vietnam.

Bacteria Shape Classification using Small-Scale Depthwise Separable CNNs.

Fast detection and classification of bacteria species play a crucial role in modern clinical microbiology systems. These processes are often performed manually by medical biologists using different shapes and morphological characteristics of bacteria species. However, it is clear that the manual taxonomy of bacteria types from microscopy images takes time and effort and is a great challenge for even experienced experts. A new revolution has been inaugurating with the development of machine learning methods to identify bacteria automatically from digital electron microscopy. In this paper, we introduce an automated model of bacteria shape classification based on Depthwise Separable Convolution Neural Networks (DS-CNNs). This architecture has excellent advantages with lower computational costs and reliable recognition accuracy. The experiment results indicate that after training with 1669 images, the proposed architecture can reach 97% validation accuracy and work well to classify three main shapes of bacteria.