Automated Detection of Canine Babesia Parasite in Blood Smear Images Using Deep Learning and Contrastive Learning Techniques Article Swipe
Dilip Kumar Baruah
,
Kuntala Boruah
,
Nagendra Nath Barman
,
Abhijit Deka
,
Arpita Bharali
,
Lukumoni Buragohain
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/parasitologia5020023
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/parasitologia5020023
This research introduces a novel method that integrates both unsupervised and supervised learning, leveraging SimCLR (Simple Framework for Contrastive Learning of Visual Representations) for self-supervised learning along with different pre-trained models to improve microscopic image classification of Babesia parasite in canines. We focused on three popular CNN architectures, namely ResNet, EfficientNet, and DenseNet, and evaluated the impact of SimCLR pre-training on their performance. A detailed comparison of the different variants of ResNet, EfficientNet, and Densenet in terms of classification accuracy and training efficiency is presented. Base models such as different variants of the ResNet, EfficientNet, and DenseNet models were utilized within the SimCLR framework. Firstly, the models were pre-trained on unlabeled images, followed by training classifiers on labeled datasets. This approach significantly improved the robustness and accuracy, demonstrating the potential benefits of combining contrastive learning with conventional supervised techniques. The highest accuracy of 97.07% was achieved by Efficientnet_b2. Thus, detection of Babesia or other hemoparasites in microscopic blood smear images could be automated with high accuracy without using a labelled dataset.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/parasitologia5020023
- https://www.mdpi.com/2673-6772/5/2/23/pdf?version=1747228035
- OA Status
- diamond
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410361175
All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.3390/parasitologia5020023Digital Object Identifier
- Title
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Automated Detection of Canine Babesia Parasite in Blood Smear Images Using Deep Learning and Contrastive Learning TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-14Full publication date if available
- Authors
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Dilip Kumar Baruah, Kuntala Boruah, Nagendra Nath Barman, Abhijit Deka, Arpita Bharali, Lukumoni BuragohainList of authors in order
- Landing page
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https://doi.org/10.3390/parasitologia5020023Publisher landing page
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https://www.mdpi.com/2673-6772/5/2/23/pdf?version=1747228035Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2673-6772/5/2/23/pdf?version=1747228035Direct OA link when available
- Concepts
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Blood smear, Parasite hosting, Babesia, Artificial intelligence, Babesiosis, Deep learning, Computer science, Pathology, Medicine, Malaria, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.on | 43, 60, 109, 116 |
| abstract_inverted_index.or | 152 |
| abstract_inverted_index.to | 31 |
| abstract_inverted_index.CNN | 46 |
| abstract_inverted_index.The | 139 |
| abstract_inverted_index.and | 10, 51, 53, 73, 80, 95, 125 |
| abstract_inverted_index.for | 17, 23 |
| abstract_inverted_index.the | 55, 67, 92, 101, 105, 123, 128 |
| abstract_inverted_index.was | 144 |
| abstract_inverted_index.Base | 85 |
| abstract_inverted_index.This | 0, 119 |
| abstract_inverted_index.both | 8 |
| abstract_inverted_index.high | 164 |
| abstract_inverted_index.such | 87 |
| abstract_inverted_index.that | 6 |
| abstract_inverted_index.were | 98, 107 |
| abstract_inverted_index.with | 27, 135, 163 |
| abstract_inverted_index.Thus, | 148 |
| abstract_inverted_index.along | 26 |
| abstract_inverted_index.blood | 157 |
| abstract_inverted_index.could | 160 |
| abstract_inverted_index.image | 34 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.other | 153 |
| abstract_inverted_index.smear | 158 |
| abstract_inverted_index.terms | 76 |
| abstract_inverted_index.their | 61 |
| abstract_inverted_index.three | 44 |
| abstract_inverted_index.using | 167 |
| abstract_inverted_index.97.07% | 143 |
| abstract_inverted_index.SimCLR | 14, 58, 102 |
| abstract_inverted_index.Visual | 21 |
| abstract_inverted_index.images | 159 |
| abstract_inverted_index.impact | 56 |
| abstract_inverted_index.method | 5 |
| abstract_inverted_index.models | 30, 86, 97, 106 |
| abstract_inverted_index.namely | 48 |
| abstract_inverted_index.within | 100 |
| abstract_inverted_index.(Simple | 15 |
| abstract_inverted_index.Babesia | 37, 151 |
| abstract_inverted_index.ResNet, | 49, 71, 93 |
| abstract_inverted_index.focused | 42 |
| abstract_inverted_index.highest | 140 |
| abstract_inverted_index.images, | 111 |
| abstract_inverted_index.improve | 32 |
| abstract_inverted_index.labeled | 117 |
| abstract_inverted_index.popular | 45 |
| abstract_inverted_index.without | 166 |
| abstract_inverted_index.DenseNet | 96 |
| abstract_inverted_index.Densenet | 74 |
| abstract_inverted_index.Firstly, | 104 |
| abstract_inverted_index.Learning | 19 |
| abstract_inverted_index.accuracy | 79, 141, 165 |
| abstract_inverted_index.achieved | 145 |
| abstract_inverted_index.approach | 120 |
| abstract_inverted_index.benefits | 130 |
| abstract_inverted_index.canines. | 40 |
| abstract_inverted_index.dataset. | 170 |
| abstract_inverted_index.detailed | 64 |
| abstract_inverted_index.followed | 112 |
| abstract_inverted_index.improved | 122 |
| abstract_inverted_index.labelled | 169 |
| abstract_inverted_index.learning | 25, 134 |
| abstract_inverted_index.parasite | 38 |
| abstract_inverted_index.research | 1 |
| abstract_inverted_index.training | 81, 114 |
| abstract_inverted_index.utilized | 99 |
| abstract_inverted_index.variants | 69, 90 |
| abstract_inverted_index.DenseNet, | 52 |
| abstract_inverted_index.Framework | 16 |
| abstract_inverted_index.accuracy, | 126 |
| abstract_inverted_index.automated | 162 |
| abstract_inverted_index.combining | 132 |
| abstract_inverted_index.datasets. | 118 |
| abstract_inverted_index.detection | 149 |
| abstract_inverted_index.different | 28, 68, 89 |
| abstract_inverted_index.evaluated | 54 |
| abstract_inverted_index.learning, | 12 |
| abstract_inverted_index.potential | 129 |
| abstract_inverted_index.unlabeled | 110 |
| abstract_inverted_index.comparison | 65 |
| abstract_inverted_index.efficiency | 82 |
| abstract_inverted_index.framework. | 103 |
| abstract_inverted_index.integrates | 7 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.leveraging | 13 |
| abstract_inverted_index.presented. | 84 |
| abstract_inverted_index.robustness | 124 |
| abstract_inverted_index.supervised | 11, 137 |
| abstract_inverted_index.Contrastive | 18 |
| abstract_inverted_index.classifiers | 115 |
| abstract_inverted_index.contrastive | 133 |
| abstract_inverted_index.microscopic | 33, 156 |
| abstract_inverted_index.pre-trained | 29, 108 |
| abstract_inverted_index.techniques. | 138 |
| abstract_inverted_index.conventional | 136 |
| abstract_inverted_index.performance. | 62 |
| abstract_inverted_index.pre-training | 59 |
| abstract_inverted_index.unsupervised | 9 |
| abstract_inverted_index.EfficientNet, | 50, 72, 94 |
| abstract_inverted_index.demonstrating | 127 |
| abstract_inverted_index.hemoparasites | 154 |
| abstract_inverted_index.significantly | 121 |
| abstract_inverted_index.architectures, | 47 |
| abstract_inverted_index.classification | 35, 78 |
| abstract_inverted_index.self-supervised | 24 |
| abstract_inverted_index.Efficientnet_b2. | 147 |
| abstract_inverted_index.Representations) | 22 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.16437237 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |