DPA-HairNet: A Dual Encoder Attention Based Network for Hair Artifact Removal in Dermoscopic Images Article Swipe
F. M. Javed Mehedi Shamrat
,
Mohd Yamani Idna Idris
,
Chowdhury Forhadul Karim
,
Xujuan Zhou
,
Raj Gururajan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3585353
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3585353
Hair artifacts in dermoscopic images significantly hinder the accurate diagnosis of melanoma and other skin conditions by obscuring critical lesion details. To address this challenge, we introduce DPA-HairNet, a novel Dual Encoder Attention-Based Network designed specifically for effective hair artifact removal while preserving lesion integrity. The model features a dual encoder architecture to separately process hair-specific and lesion-specific features, an attention mechanism to prioritize diagnostically relevant regions, and a dual-output design that generates precise hair segmentation masks and high-quality reconstructed images. This study leverages the ISIC 2018 and HAM10000 datasets, incorporating advanced data augmentation techniques to enhance dataset diversity and ensure robust model training. DPA-HairNet was evaluated against six state-of-the-art segmentation models using comprehensive metrics, including Accuracy, Dice Coefficient, Jaccard Index, PSNR, MAE, Specificity, Precision, Recall, and F1-Score, where the proposed model outperformed all models. Furthermore, classification performance was assessed before and after hair artifact removal using twelve pre-trained classifiers, demonstrating significant improvements in diagnostic accuracy. Explainable AI techniques, Grad-CAM, UMAP and attention heatmaps, were employed to interpret and validate the model’s focus. These results underscore DPA-HairNet’s effectiveness and potential integration into clinical workflows to enhance dermoscopic image analysis and diagnosis. Future work will explore generalization to additional artifact types and optimization for real-time clinical deployment.
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https://doi.org/10.1109/access.2025.3585353Digital Object Identifier
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DPA-HairNet: A Dual Encoder Attention Based Network for Hair Artifact Removal in Dermoscopic ImagesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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F. M. Javed Mehedi Shamrat, Mohd Yamani Idna Idris, Chowdhury Forhadul Karim, Xujuan Zhou, Raj GururajanList of authors in order
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https://doi.org/10.1109/access.2025.3585353Publisher landing page
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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://doi.org/10.1109/access.2025.3585353Direct OA link when available
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Artifact (error), Computer science, Dual (grammatical number), Encoder, Computer vision, Artificial intelligence, Art, Operating system, LiteratureTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 84 |
| abstract_inverted_index.a | 28, 48, 68 |
| abstract_inverted_index.AI | 157 |
| abstract_inverted_index.To | 21 |
| abstract_inverted_index.an | 59 |
| abstract_inverted_index.by | 16 |
| abstract_inverted_index.in | 2, 153 |
| abstract_inverted_index.of | 10 |
| abstract_inverted_index.to | 52, 62, 95, 166, 184, 196 |
| abstract_inverted_index.we | 25 |
| abstract_inverted_index.The | 45 |
| abstract_inverted_index.all | 133 |
| abstract_inverted_index.and | 12, 56, 67, 77, 87, 99, 126, 141, 161, 168, 178, 189, 200 |
| abstract_inverted_index.for | 36, 202 |
| abstract_inverted_index.six | 108 |
| abstract_inverted_index.the | 7, 84, 129, 170 |
| abstract_inverted_index.was | 105, 138 |
| abstract_inverted_index.2018 | 86 |
| abstract_inverted_index.Dice | 117 |
| abstract_inverted_index.Dual | 30 |
| abstract_inverted_index.Hair | 0 |
| abstract_inverted_index.ISIC | 85 |
| abstract_inverted_index.MAE, | 122 |
| abstract_inverted_index.This | 81 |
| abstract_inverted_index.UMAP | 160 |
| abstract_inverted_index.data | 92 |
| abstract_inverted_index.dual | 49 |
| abstract_inverted_index.hair | 38, 74, 143 |
| abstract_inverted_index.into | 181 |
| abstract_inverted_index.skin | 14 |
| abstract_inverted_index.that | 71 |
| abstract_inverted_index.this | 23 |
| abstract_inverted_index.were | 164 |
| abstract_inverted_index.will | 193 |
| abstract_inverted_index.work | 192 |
| abstract_inverted_index.PSNR, | 121 |
| abstract_inverted_index.These | 173 |
| abstract_inverted_index.after | 142 |
| abstract_inverted_index.image | 187 |
| abstract_inverted_index.masks | 76 |
| abstract_inverted_index.model | 46, 102, 131 |
| abstract_inverted_index.novel | 29 |
| abstract_inverted_index.other | 13 |
| abstract_inverted_index.study | 82 |
| abstract_inverted_index.types | 199 |
| abstract_inverted_index.using | 112, 146 |
| abstract_inverted_index.where | 128 |
| abstract_inverted_index.while | 41 |
| abstract_inverted_index.Future | 191 |
| abstract_inverted_index.Index, | 120 |
| abstract_inverted_index.before | 140 |
| abstract_inverted_index.design | 70 |
| abstract_inverted_index.ensure | 100 |
| abstract_inverted_index.focus. | 172 |
| abstract_inverted_index.hinder | 6 |
| abstract_inverted_index.images | 4 |
| abstract_inverted_index.lesion | 19, 43 |
| abstract_inverted_index.models | 111 |
| abstract_inverted_index.robust | 101 |
| abstract_inverted_index.twelve | 147 |
| abstract_inverted_index.Encoder | 31 |
| abstract_inverted_index.Jaccard | 119 |
| abstract_inverted_index.Network | 33 |
| abstract_inverted_index.Recall, | 125 |
| abstract_inverted_index.address | 22 |
| abstract_inverted_index.against | 107 |
| abstract_inverted_index.dataset | 97 |
| abstract_inverted_index.encoder | 50 |
| abstract_inverted_index.enhance | 96, 185 |
| abstract_inverted_index.explore | 194 |
| abstract_inverted_index.images. | 80 |
| abstract_inverted_index.models. | 134 |
| abstract_inverted_index.precise | 73 |
| abstract_inverted_index.process | 54 |
| abstract_inverted_index.removal | 40, 145 |
| abstract_inverted_index.results | 174 |
| abstract_inverted_index.HAM10000 | 88 |
| abstract_inverted_index.accurate | 8 |
| abstract_inverted_index.advanced | 91 |
| abstract_inverted_index.analysis | 188 |
| abstract_inverted_index.artifact | 39, 144, 198 |
| abstract_inverted_index.assessed | 139 |
| abstract_inverted_index.clinical | 182, 204 |
| abstract_inverted_index.critical | 18 |
| abstract_inverted_index.designed | 34 |
| abstract_inverted_index.details. | 20 |
| abstract_inverted_index.employed | 165 |
| abstract_inverted_index.features | 47 |
| abstract_inverted_index.melanoma | 11 |
| abstract_inverted_index.metrics, | 114 |
| abstract_inverted_index.proposed | 130 |
| abstract_inverted_index.regions, | 66 |
| abstract_inverted_index.relevant | 65 |
| abstract_inverted_index.validate | 169 |
| abstract_inverted_index.Accuracy, | 116 |
| abstract_inverted_index.F1-Score, | 127 |
| abstract_inverted_index.Grad-CAM, | 159 |
| abstract_inverted_index.accuracy. | 155 |
| abstract_inverted_index.artifacts | 1 |
| abstract_inverted_index.attention | 60, 162 |
| abstract_inverted_index.datasets, | 89 |
| abstract_inverted_index.diagnosis | 9 |
| abstract_inverted_index.diversity | 98 |
| abstract_inverted_index.effective | 37 |
| abstract_inverted_index.evaluated | 106 |
| abstract_inverted_index.features, | 58 |
| abstract_inverted_index.generates | 72 |
| abstract_inverted_index.heatmaps, | 163 |
| abstract_inverted_index.including | 115 |
| abstract_inverted_index.interpret | 167 |
| abstract_inverted_index.introduce | 26 |
| abstract_inverted_index.leverages | 83 |
| abstract_inverted_index.mechanism | 61 |
| abstract_inverted_index.obscuring | 17 |
| abstract_inverted_index.potential | 179 |
| abstract_inverted_index.real-time | 203 |
| abstract_inverted_index.training. | 103 |
| abstract_inverted_index.workflows | 183 |
| abstract_inverted_index.Precision, | 124 |
| abstract_inverted_index.additional | 197 |
| abstract_inverted_index.challenge, | 24 |
| abstract_inverted_index.conditions | 15 |
| abstract_inverted_index.diagnosis. | 190 |
| abstract_inverted_index.diagnostic | 154 |
| abstract_inverted_index.integrity. | 44 |
| abstract_inverted_index.preserving | 42 |
| abstract_inverted_index.prioritize | 63 |
| abstract_inverted_index.separately | 53 |
| abstract_inverted_index.techniques | 94 |
| abstract_inverted_index.underscore | 175 |
| abstract_inverted_index.DPA-HairNet | 104 |
| abstract_inverted_index.Explainable | 156 |
| abstract_inverted_index.deployment. | 205 |
| abstract_inverted_index.dermoscopic | 3, 186 |
| abstract_inverted_index.dual-output | 69 |
| abstract_inverted_index.integration | 180 |
| abstract_inverted_index.performance | 137 |
| abstract_inverted_index.pre-trained | 148 |
| abstract_inverted_index.significant | 151 |
| abstract_inverted_index.techniques, | 158 |
| abstract_inverted_index.Coefficient, | 118 |
| abstract_inverted_index.DPA-HairNet, | 27 |
| abstract_inverted_index.Furthermore, | 135 |
| abstract_inverted_index.Specificity, | 123 |
| abstract_inverted_index.architecture | 51 |
| abstract_inverted_index.augmentation | 93 |
| abstract_inverted_index.classifiers, | 149 |
| abstract_inverted_index.high-quality | 78 |
| abstract_inverted_index.improvements | 152 |
| abstract_inverted_index.optimization | 201 |
| abstract_inverted_index.outperformed | 132 |
| abstract_inverted_index.segmentation | 75, 110 |
| abstract_inverted_index.specifically | 35 |
| abstract_inverted_index.comprehensive | 113 |
| abstract_inverted_index.demonstrating | 150 |
| abstract_inverted_index.effectiveness | 177 |
| abstract_inverted_index.hair-specific | 55 |
| abstract_inverted_index.incorporating | 90 |
| abstract_inverted_index.reconstructed | 79 |
| abstract_inverted_index.significantly | 5 |
| abstract_inverted_index.classification | 136 |
| abstract_inverted_index.diagnostically | 64 |
| abstract_inverted_index.generalization | 195 |
| abstract_inverted_index.model’s | 171 |
| abstract_inverted_index.Attention-Based | 32 |
| abstract_inverted_index.lesion-specific | 57 |
| abstract_inverted_index.state-of-the-art | 109 |
| abstract_inverted_index.DPA-HairNet’s | 176 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/5 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Gender equality |
| citation_normalized_percentile.value | 0.3703081 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |