An Automated Deep Learning Approach for Bacterial Image Classification
This work addresses the need for faster and more accurate bacterial classification in clinical microbiology, though it is incremental as it applies an existing method to a specific domain.
The authors tackled the problem of automating bacterial species classification from microscopic images, which is typically done manually and is time-consuming, by proposing a deep learning approach using ResNet-50 with transfer learning, achieving an average classification accuracy of 99.2%.
Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even experienced biologists. In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. The ResNet-50 pre-trained CNN architecture has been used to classify digital bacteria images into 33 categories. The transfer learning technique was employed to accelerate the training process of the network and improve the classification performance of the network. The proposed method achieved an average classification accuracy of 99.2%. The experimental results demonstrate that the proposed technique surpasses state-of-the-art methods in the literature and can be used for any type of bacteria classification tasks.