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.