Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization Article Swipe
Fareed Ahmad
,
Muhammad Usman Ghani Khan
,
Ahsen Tahir
,
Farhan Masud
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-023-05398-7
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-023-05398-7
Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12859-023-05398-7
- https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-023-05398-7
- OA Status
- gold
- Cited By
- 13
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382797888
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- OpenAlex ID
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https://openalex.org/W4382797888Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12859-023-05398-7Digital Object Identifier
- Title
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Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimizationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-01Full publication date if available
- Authors
-
Fareed Ahmad, Muhammad Usman Ghani Khan, Ahsen Tahir, Farhan MasudList of authors in order
- Landing page
-
https://doi.org/10.1186/s12859-023-05398-7Publisher landing page
- PDF URL
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https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-023-05398-7Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-023-05398-7Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Convolutional neural network, F1 score, Robustness (evolution), Pattern recognition (psychology), Random forest, Recall, Ensemble forecasting, Deep learning, Ensemble learning, Machine learning, Biology, Gene, Philosophy, Biochemistry, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
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2025: 5, 2024: 6, 2023: 2Per-year citation counts (last 5 years)
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29Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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