AGAR a Microbial Colony Dataset for Deep Learning Detection

Majchrowska, Sylwia; Pawłowski, Jarosław; Guła, Grzegorz; Bonus, T.; Hanas, Agata; Loch, Adam; Pawlak, Agnieszka; Roszkowiak, Justyna; Golan, T.; Drulis-Kawa, Zuzanna

doi:10.21203/rs.3.rs-668667/v1

Cited by 13 publications

(2 citation statements)

References 31 publications

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“…In the study by Majchrowska et al ( 2021 ) the group contributed a set of 18,000 images of five species of both single and mixed culture micro-organisms for the purpose of building a deep learning network (Majchrowska et al, 2021 ). The dataset, known as the Annotated Germs for Automated Recognition (AGAR) dataset, is comprised of five species from different bacterial groups, namely, five representatives from different bacterial groups, namely: S. aureus subsp.…”

Section: Literature Surveymentioning

confidence: 99%

“…In total, 336,442 colonies of the five microbial species distributed over the countable class were labeled. Additionally, Majchrowska et al ( 2021 ) developed Convolutional Neural Network (CNN) models to accurately classify and count bacterial colonies within the AGAR dataset. The basis for these models stemmed from the work of Girshick et al ( 2014 ), who introduced the concept of Region-Based Convolutional Neural Networks (R-CNN) with a focus on object detection.…”

Section: Literature Surveymentioning

confidence: 99%

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Machine learning algorithms in microbial classification: a comparative analysis

Wu,

Gadsden

2023

Front. Artif. Intell.

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This research paper presents an overview of contemporary machine learning methodologies and their utilization in the domain of healthcare and the prevention of infectious diseases, specifically focusing on the classification and identification of bacterial species. As deep learning techniques have gained prominence in the healthcare sector, a diverse array of architectural models has emerged. Through a comprehensive review of pertinent literature, multiple studies employing machine learning algorithms in the context of microbial diagnosis and classification are examined. Each investigation entails a tabulated presentation of data, encompassing details about the training and validation datasets, specifications of the machine learning and deep learning techniques employed, as well as the evaluation metrics utilized to gauge algorithmic performance. Notably, Convolutional Neural Networks have been the predominant selection for image classification tasks by machine learning practitioners over the last decade. This preference stems from their ability to autonomously extract pertinent and distinguishing features with minimal human intervention. A range of CNN architectures have been developed and effectively applied in the realm of image classification. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. This method involves repurposing the knowledge gleaned from solving alternate image classification challenges to accurately classify microbial images. Consequently, the necessity for extensive and varied training data is significantly mitigated. This study undertakes a comparative assessment of various popular pre-trained CNN architectures for the classification of bacteria. The dataset employed is composed of approximately 660 images, representing 33 bacterial species. To enhance dataset diversity, data augmentation is implemented, followed by evaluation on multiple models including AlexNet, VGGNet, Inception networks, Residual Networks, and Densely Connected Convolutional Networks. The results indicate that the DenseNet-121 architecture yields the optimal performance, achieving a peak accuracy of 99.08%, precision of 99.06%, recall of 99.00%, and an F1-score of 98.99%. By demonstrating the proficiency of the DenseNet-121 model on a comparatively modest dataset, this study underscores the viability of transfer learning in the healthcare sector for precise and efficient microbial identification. These findings contribute to the ongoing endeavors aimed at harnessing machine learning techniques to enhance healthcare methodologies and bolster infectious disease prevention practices.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Machine learning algorithms in microbial classification: a comparative analysis

Wu,

Gadsden

2023

Front. Artif. Intell.

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Deep Neural Networks Approach to Microbial Colony Detection—A Comparative Analysis

Majchrowska

Pawłowski

Czerep

et al. 2022

Digital Interaction and Machine Intelligence

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Counting microbial colonies is a fundamental task in microbiology and has many applications in numerous industry branches. Despite this, current studies towards automatic microbial counting using artificial intelligence are hardly comparable due to the lack of unified methodology and the availability of large datasets. The recently introduced AGAR dataset is the answer to the second need, but the research carried out is still not exhaustive. To tackle this problem, we compared the performance of three well-known deep learning approaches for object detection on the AGAR dataset, namely two-stage, one-stage, and transformer-based neural networks. The achieved results may serve as a benchmark for future experiments.

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