A Microbiological Image Repository of Escherichia coli and Klebsiella pneumoniae Bacterial Colonies on MacConkey Agar

This dataset consists of images of two types of bacterial strains streaked on MacConkey agar plates. The images of the bacterial colonies were taken under two different shooting conditions, “controlled” and “uncontrolled” as described in “steps to reproduce” section. These bacterial strains are:
1- Escherichia coli (E. coli): the number of images under controlled conditions is (168) and the number of images under uncontrolled conditions is (3532).
2- Klebsiella pneumoniae (K. pneumoniae): the number of images under controlled conditions is (152) and the number of images under uncontrolled conditions is (3513).

IN THE REPOSITORY, YOU WILL FIND:
Group 1 consists of images taken from 39 and 36 plates of K. pneumoniae and E. coli respectively, under controlled and uncontrolled conditions.
Group2 consists of images taken from 25 plates of K. pneumoniae and E. coli, under controlled and uncontrolled conditions.
An excel sheet detailing the numbers of images in the folders.

NOTABLE FINDING:
Baseline CNNs trained on this data achieved high accuracy, indicating that phone images provide sufficient discriminative signal without expert inspection. Please refer to:
1) S. A. Nagro et al., "Automatic Identification of Single Bacterial Colonies Using Deep and Transfer Learning," in IEEE Access, vol. 10, pp. 120181-120190, 2022, DOI: https://doi.org/10.1109/ACCESS.2022.3221958
2) M. Kutbi et al., "Leveraging Smartphone Imaging and Deep Transfer Learning for Bacterial Colony Classification: From Uncontrolled to Controlled Settings," in IEEE Access, doi: https://doi.org/10.1109/ACCESS.2025.3625648

HOW THIS DATASET CAN BE USED:
This dataset can be utilized in any research interested in recognizing different features of bacterial colonies. The dataset can also be used to train and evaluate deep learning models for colony classification, while also supporting studies on robustness, generalization, and practical deployment, to advance computer vision and AI applications in microbiology. By combining clinically important bacteria with controlled and uncontrolled imaging, the dataset offers a realistic and accessible resource for researchers interested in developing AI methods that perform reliably in laboratory and non-laboratory environments.

IMPORTANT NOTE:
This dataset was expanded to include different types of bacterial strains and culture media which can be found in [DOI: 10.17632/v54x8jdx5x.1]

IF YOU USE THIS DATASET, PLEASE REFERENCE THE FOLLOWING:
1. DOI: https://doi.org/10.1109/ACCESS.2022.3221958
2. DOI: https://doi.org/10.1109/ACCESS.2025.3625648
3. DOI: https://doi.org/10.17632/kx6gz3wmcf.1
4. DOI: https://doi.org/10.17632/v54x8jdx5x.1