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Efficient deep learning architectures for fast identification of bacterial strains in resource-constrained devices

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Abstract

This work presents twelve fine-tuned deep learning architectures to solve the bacterial classification problem over the digital image of bacterial species dataset. The base architectures were mainly published as mobile or efficient solutions to the ImageNet challenge, and all experiments presented in this work consisted in making several modifications to the original designs, in order to make them able to solve the bacterial classification problem by using fine-tuning and transfer learning techniques. This work also proposes a novel data augmentation technique for this dataset, which is based on the idea of artificial zooming, strongly increasing the performance of every tested architecture, even doubling it in some cases. In order to get robust and complete evaluations, all experiments were performed with 10-fold cross-validation and evaluated with five different metrics: top-1 and top-5 accuracy, precision, recall, and F1 score. This paper presents a complete comparison of the twelve different architectures, cross-validated with the original and the augmented version of the dataset, the results are also compared with several literature methods. Overall, eight of the eleven architectures surpassed the 0.95 score in top-1 accuracy with our data augmentation method, being 0.9738 the highest top-1 accuracy. The impact of the data augmentation technique is reported with relative improvement scores.

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Code Availability

The software requirements, Jupyter notebooks, Python scripts and detailed instructions to replicate our experiments are available on the following GitHub repository: github.com/gallardorafael/EfficientMobileDL_Bacterial

Notes

  1. http://misztal.edu.pl/software/databases/dibas/

  2. https://github.com/lukemelas/EfficientNet-PyTorch

  3. https://github.com/d-li14/mobilenetv3.pytorch

  4. https://github.com/forresti/SqueezeNet/

  5. https://jupyter.org/

  6. https://www.passmark.com/

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Funding

This work did not receive funding of any kind.

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Authors and Affiliations

Authors

Contributions

Rafael Gallardo García and Sofía Jarquín Rodríguez contributed to the study conception, design, experimentation and writing of the manuscript. Chemical and biological background of bacterial identification provided in this paper was performed by Sofía Jarquín Rodríguez. Rafael Gallardo García worked on data processing, system design, programming, training and evaluation related tasks. Beatriz Beltrán Martínez and Carlos Hernández Gracidas provided useful and complete feedback to ensure strong and robust research results, also, they contributed with detailed revisions of the paper structure, content and language-quality. Rodolfo Martínez Torres provided feedback and assistance for some engineering-related tasks. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rafael Gallardo García.

Ethics declarations

Ethics approval

As the original data was acquired in [40], none of the authors of this work were exposed to dangerous bacteria, biological material or to dangers of any kind. With the aforementioned, we consider that this section is not applicable to this paper.

Conflict of Interests

The authors have declared that no conflict of interests or competing interests exist.

Additional information

Availability of data and material

In the Data Availability section of their paper [40], authors of the DIBaS dataset provided the following link to access the data: http://misztal.edu.pl/software/databases/dibas

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Gallardo García, R., Jarquín Rodríguez, S., Beltrán Martínez, B. et al. Efficient deep learning architectures for fast identification of bacterial strains in resource-constrained devices. Multimed Tools Appl 81, 39915–39944 (2022). https://doi.org/10.1007/s11042-022-13022-8

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  • DOI: https://doi.org/10.1007/s11042-022-13022-8

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