DualBranch‐FusionNet: A Hybrid CNN‐Transformer Architecture for Cervical Cell Image Classification Article Swipe
Chuanyun Xu
,
Simon Huang
,
Yang Zhang
,
Die Hu
,
Yisha Sun
,
Gang Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/ima.70101
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/ima.70101
Cervical cancer screening relies on accurate cell classification. Approaches based on Convolutional Neural Networks (CNNs) have proven effective in addressing the task. However, these approaches suffer from two main challenges. First, they may introduce bias into models due to variations in cell morphology and color. Second, they may struggle to capture broader contextual information as CNNs primarily focus on local pixel information. To address these issues, we present a novel hybrid model named DualBranch‐FusionNet, which combines CNNs for local feature extraction with Transformers for capturing global contextual information to improve cervical cell classification accuracy. The proposed method adopts the three‐fold ideas. First, concerning the CNN branch, it introduces Omni‐dimensional Dynamic Convolution (ODConv) to adaptively extract detailed features across multiple dimensions and designs an Adaptive Channel Modulation (ACM) mechanism to dynamically emphasize critical feature channels. Second, regarding the Transformer branch, it designs a Dynamic Query‐Aware Sparse Attention (DQSA) mechanism to effectively filter out less relevant key‐value pairs over a larger receptive field, thereby reducing the interference of irrelevant information. Third, it adopts a fusion strategy, the Simple Fusion Module (SFM), to produce more comprehensive feature representations, leading to improved cervical cell classification accuracy. The proposed model was validated on two datasets: the Mendeley LBC and the Tianchi Cervical Cancer Risk Intelligent Diagnosis Challenge datasets, achieving Accuracies of 99.07% and 99.12%, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ima.70101
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ima.70101
- OA Status
- bronze
- Cited By
- 1
- References
- 46
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.1002/ima.70101Digital Object Identifier
- Title
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DualBranch‐FusionNet: A Hybrid CNN‐Transformer Architecture for Cervical Cell Image ClassificationWork 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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2025-05-01Full publication date if available
- Authors
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Chuanyun Xu, Simon Huang, Yang Zhang, Die Hu, Yisha Sun, Gang LiList of authors in order
- Landing page
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https://doi.org/10.1002/ima.70101Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ima.70101Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ima.70101Direct OA link when available
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Computer science, Transformer, Architecture, Artificial intelligence, Pattern recognition (psychology), Electrical engineering, Engineering, Voltage, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 46 |
| abstract_inverted_index.a | 69, 142, 158, 172 |
| abstract_inverted_index.To | 63 |
| abstract_inverted_index.an | 123 |
| abstract_inverted_index.as | 55 |
| abstract_inverted_index.in | 19, 41 |
| abstract_inverted_index.it | 107, 140, 170 |
| abstract_inverted_index.of | 166, 216 |
| abstract_inverted_index.on | 5, 11, 59, 198 |
| abstract_inverted_index.to | 39, 50, 89, 113, 129, 149, 180, 187 |
| abstract_inverted_index.we | 67 |
| abstract_inverted_index.CNN | 105 |
| abstract_inverted_index.LBC | 203 |
| abstract_inverted_index.The | 95, 193 |
| abstract_inverted_index.and | 44, 121, 204, 218 |
| abstract_inverted_index.due | 38 |
| abstract_inverted_index.for | 78, 84 |
| abstract_inverted_index.may | 33, 48 |
| abstract_inverted_index.out | 152 |
| abstract_inverted_index.the | 21, 99, 104, 137, 164, 175, 201, 205 |
| abstract_inverted_index.two | 28, 199 |
| abstract_inverted_index.was | 196 |
| abstract_inverted_index.CNNs | 56, 77 |
| abstract_inverted_index.Risk | 209 |
| abstract_inverted_index.bias | 35 |
| abstract_inverted_index.cell | 7, 42, 92, 190 |
| abstract_inverted_index.from | 27 |
| abstract_inverted_index.have | 16 |
| abstract_inverted_index.into | 36 |
| abstract_inverted_index.less | 153 |
| abstract_inverted_index.main | 29 |
| abstract_inverted_index.more | 182 |
| abstract_inverted_index.over | 157 |
| abstract_inverted_index.they | 32, 47 |
| abstract_inverted_index.with | 82 |
| abstract_inverted_index.(ACM) | 127 |
| abstract_inverted_index.based | 10 |
| abstract_inverted_index.focus | 58 |
| abstract_inverted_index.local | 60, 79 |
| abstract_inverted_index.model | 72, 195 |
| abstract_inverted_index.named | 73 |
| abstract_inverted_index.novel | 70 |
| abstract_inverted_index.pairs | 156 |
| abstract_inverted_index.pixel | 61 |
| abstract_inverted_index.task. | 22 |
| abstract_inverted_index.these | 24, 65 |
| abstract_inverted_index.which | 75 |
| abstract_inverted_index.(CNNs) | 15 |
| abstract_inverted_index.(DQSA) | 147 |
| abstract_inverted_index.(SFM), | 179 |
| abstract_inverted_index.99.07% | 217 |
| abstract_inverted_index.Cancer | 208 |
| abstract_inverted_index.First, | 31, 102 |
| abstract_inverted_index.Fusion | 177 |
| abstract_inverted_index.Module | 178 |
| abstract_inverted_index.Neural | 13 |
| abstract_inverted_index.Simple | 176 |
| abstract_inverted_index.Sparse | 145 |
| abstract_inverted_index.Third, | 169 |
| abstract_inverted_index.across | 118 |
| abstract_inverted_index.adopts | 98, 171 |
| abstract_inverted_index.cancer | 2 |
| abstract_inverted_index.color. | 45 |
| abstract_inverted_index.field, | 161 |
| abstract_inverted_index.filter | 151 |
| abstract_inverted_index.fusion | 173 |
| abstract_inverted_index.global | 86 |
| abstract_inverted_index.hybrid | 71 |
| abstract_inverted_index.ideas. | 101 |
| abstract_inverted_index.larger | 159 |
| abstract_inverted_index.method | 97 |
| abstract_inverted_index.models | 37 |
| abstract_inverted_index.proven | 17 |
| abstract_inverted_index.relies | 4 |
| abstract_inverted_index.suffer | 26 |
| abstract_inverted_index.99.12%, | 219 |
| abstract_inverted_index.Channel | 125 |
| abstract_inverted_index.Dynamic | 110, 143 |
| abstract_inverted_index.Second, | 46, 135 |
| abstract_inverted_index.Tianchi | 206 |
| abstract_inverted_index.address | 64 |
| abstract_inverted_index.branch, | 106, 139 |
| abstract_inverted_index.broader | 52 |
| abstract_inverted_index.capture | 51 |
| abstract_inverted_index.designs | 122, 141 |
| abstract_inverted_index.extract | 115 |
| abstract_inverted_index.feature | 80, 133, 184 |
| abstract_inverted_index.improve | 90 |
| abstract_inverted_index.issues, | 66 |
| abstract_inverted_index.leading | 186 |
| abstract_inverted_index.present | 68 |
| abstract_inverted_index.produce | 181 |
| abstract_inverted_index.thereby | 162 |
| abstract_inverted_index.(ODConv) | 112 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.Adaptive | 124 |
| abstract_inverted_index.Cervical | 1, 207 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.Mendeley | 202 |
| abstract_inverted_index.Networks | 14 |
| abstract_inverted_index.accurate | 6 |
| abstract_inverted_index.cervical | 91, 189 |
| abstract_inverted_index.combines | 76 |
| abstract_inverted_index.critical | 132 |
| abstract_inverted_index.detailed | 116 |
| abstract_inverted_index.features | 117 |
| abstract_inverted_index.improved | 188 |
| abstract_inverted_index.multiple | 119 |
| abstract_inverted_index.proposed | 96, 194 |
| abstract_inverted_index.reducing | 163 |
| abstract_inverted_index.relevant | 154 |
| abstract_inverted_index.struggle | 49 |
| abstract_inverted_index.Attention | 146 |
| abstract_inverted_index.Challenge | 212 |
| abstract_inverted_index.Diagnosis | 211 |
| abstract_inverted_index.accuracy. | 94, 192 |
| abstract_inverted_index.achieving | 214 |
| abstract_inverted_index.capturing | 85 |
| abstract_inverted_index.channels. | 134 |
| abstract_inverted_index.datasets, | 213 |
| abstract_inverted_index.datasets: | 200 |
| abstract_inverted_index.effective | 18 |
| abstract_inverted_index.emphasize | 131 |
| abstract_inverted_index.introduce | 34 |
| abstract_inverted_index.mechanism | 128, 148 |
| abstract_inverted_index.primarily | 57 |
| abstract_inverted_index.receptive | 160 |
| abstract_inverted_index.regarding | 136 |
| abstract_inverted_index.screening | 3 |
| abstract_inverted_index.strategy, | 174 |
| abstract_inverted_index.validated | 197 |
| abstract_inverted_index.Accuracies | 215 |
| abstract_inverted_index.Approaches | 9 |
| abstract_inverted_index.Modulation | 126 |
| abstract_inverted_index.adaptively | 114 |
| abstract_inverted_index.addressing | 20 |
| abstract_inverted_index.approaches | 25 |
| abstract_inverted_index.concerning | 103 |
| abstract_inverted_index.contextual | 53, 87 |
| abstract_inverted_index.dimensions | 120 |
| abstract_inverted_index.extraction | 81 |
| abstract_inverted_index.introduces | 108 |
| abstract_inverted_index.irrelevant | 167 |
| abstract_inverted_index.morphology | 43 |
| abstract_inverted_index.variations | 40 |
| abstract_inverted_index.Convolution | 111 |
| abstract_inverted_index.Intelligent | 210 |
| abstract_inverted_index.Transformer | 138 |
| abstract_inverted_index.challenges. | 30 |
| abstract_inverted_index.dynamically | 130 |
| abstract_inverted_index.effectively | 150 |
| abstract_inverted_index.information | 54, 88 |
| abstract_inverted_index.key‐value | 155 |
| abstract_inverted_index.Transformers | 83 |
| abstract_inverted_index.information. | 62, 168 |
| abstract_inverted_index.interference | 165 |
| abstract_inverted_index.three‐fold | 100 |
| abstract_inverted_index.Convolutional | 12 |
| abstract_inverted_index.Query‐Aware | 144 |
| abstract_inverted_index.comprehensive | 183 |
| abstract_inverted_index.respectively. | 220 |
| abstract_inverted_index.classification | 93, 191 |
| abstract_inverted_index.classification. | 8 |
| abstract_inverted_index.representations, | 185 |
| abstract_inverted_index.Omni‐dimensional | 109 |
| abstract_inverted_index.DualBranch‐FusionNet, | 74 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.94683565 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |