# An Automated Deep Learning Approach for Bacterial Image Classification

arXiv: https://arxiv.org/abs/1912.08765 · HTML: https://mlsanity.com/paper/1912.08765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [AGAR a microbial colony dataset for deep learning detection](https://mlsanity.com/paper/2108.01234/) (2021)
- [Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained Devices](https://mlsanity.com/paper/2106.06505/) (2021)
- [Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset](https://mlsanity.com/paper/2208.10737/) (2022)

## Abstract (arXiv)

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.