Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation Article Swipe
Jianhua Zhao
,
Harvey Lui
,
Sunil Kalia
,
Tim K. Lee
,
Haishan Zeng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1320220
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1320220
Background Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1–3% improvement (p<0.001, Wilcoxon test). Conclusions Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2–4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.
Related Topics
Concepts
Receiver operating characteristic
Artificial intelligence
Linear discriminant analysis
Pattern recognition (psychology)
Convolutional neural network
Principal component analysis
Support vector machine
Computer science
Raman spectroscopy
Pooling
Basal cell carcinoma
Mathematics
Machine learning
Medicine
Pathology
Physics
Basal cell
Optics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2024.1320220
- https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1320220/pdf
- OA Status
- gold
- Cited By
- 16
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399821708
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399821708Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fonc.2024.1320220Digital Object Identifier
- Title
-
Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-06-19Full publication date if available
- Authors
-
Jianhua Zhao, Harvey Lui, Sunil Kalia, Tim K. Lee, Haishan ZengList of authors in order
- Landing page
-
https://doi.org/10.3389/fonc.2024.1320220Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1320220/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1320220/pdfDirect OA link when available
- Concepts
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Receiver operating characteristic, Artificial intelligence, Linear discriminant analysis, Pattern recognition (psychology), Convolutional neural network, Principal component analysis, Support vector machine, Computer science, Raman spectroscopy, Pooling, Basal cell carcinoma, Mathematics, Machine learning, Medicine, Pathology, Physics, Basal cell, OpticsTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 3Per-year citation counts (last 5 years)
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77Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.studies | 3 |
| abstract_inverted_index.support | 185 |
| abstract_inverted_index.(1D-CNN) | 88 |
| abstract_inverted_index.Patients | 45 |
| abstract_inverted_index.Wilcoxon | 255, 274 |
| abstract_inverted_index.analyses | 258 |
| abstract_inverted_index.analysis | 176, 183 |
| abstract_inverted_index.dataset, | 127 |
| abstract_inverted_index.improved | 36, 234, 281, 298 |
| abstract_inverted_index.included | 55 |
| abstract_inverted_index.learning | 168, 264, 292, 319 |
| abstract_inverted_index.logistic | 190 |
| abstract_inverted_index.networks | 41, 87, 145, 288, 314 |
| abstract_inverted_index.original | 217 |
| abstract_inverted_index.parallel | 116 |
| abstract_inverted_index.previous | 2 |
| abstract_inverted_index.randomly | 100 |
| abstract_inverted_index.receiver | 151 |
| abstract_inverted_index.repeated | 112 |
| abstract_inverted_index.slightly | 315 |
| abstract_inverted_index.spectral | 93, 132, 134, 310 |
| abstract_inverted_index.squamous | 69 |
| abstract_inverted_index.training | 102, 126, 218, 252 |
| abstract_inverted_index.(1D-CNN), | 222, 237 |
| abstract_inverted_index.(1D-GAN). | 146 |
| abstract_inverted_index.(PC-LDA), | 184, 226, 241 |
| abstract_inverted_index.(PLS-DA), | 177, 224, 239 |
| abstract_inverted_index.Different | 118 |
| abstract_inverted_index.cancerous | 61 |
| abstract_inverted_index.carcinoma | 71 |
| abstract_inverted_index.combining | 38 |
| abstract_inverted_index.component | 179 |
| abstract_inverted_index.detection | 15, 32, 324 |
| abstract_inverted_index.determine | 28 |
| abstract_inverted_index.developed | 90 |
| abstract_inverted_index.evaluated | 194 |
| abstract_inverted_index.including | 128, 170 |
| abstract_inverted_index.objective | 22 |
| abstract_inverted_index.operating | 152 |
| abstract_inverted_index.principal | 178 |
| abstract_inverted_index.shifting. | 311 |
| abstract_inverted_index.splitting | 201 |
| abstract_inverted_index.(melanoma, | 65 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.approaches | 265, 320 |
| abstract_inverted_index.carcinoma, | 68 |
| abstract_inverted_index.comparison | 196 |
| abstract_inverted_index.computing. | 117 |
| abstract_inverted_index.containing | 59 |
| abstract_inverted_index.diagnostic | 164 |
| abstract_inverted_index.generative | 143 |
| abstract_inverted_index.keratosis) | 74 |
| abstract_inverted_index.regression | 191 |
| abstract_inverted_index.seborrheic | 82 |
| abstract_inverted_index.strategies | 121 |
| abstract_inverted_index.stratified | 96 |
| abstract_inverted_index.techniques | 293 |
| abstract_inverted_index.validation | 104 |
| abstract_inverted_index.Conclusions | 276 |
| abstract_inverted_index.adversarial | 144 |
| abstract_inverted_index.approaches, | 169 |
| abstract_inverted_index.combination | 135 |
| abstract_inverted_index.implemented | 123 |
| abstract_inverted_index.improvement | 272 |
| abstract_inverted_index.keratosis). | 83 |
| abstract_inverted_index.performance | 283, 300 |
| abstract_inverted_index.sensitivity | 18 |
| abstract_inverted_index.synthesized | 138 |
| abstract_inverted_index.(melanocytic | 79 |
| abstract_inverted_index.0.525±0.045 | 230 |
| abstract_inverted_index.0.864±0.027 | 227 |
| abstract_inverted_index.0.870±0.028 | 223 |
| abstract_inverted_index.0.875±0.033 | 225 |
| abstract_inverted_index.0.886±0.022 | 221 |
| abstract_inverted_index.0.895±0.022 | 240 |
| abstract_inverted_index.0.897±0.021 | 245 |
| abstract_inverted_index.0.899±0.022 | 238 |
| abstract_inverted_index.0.901±0.020 | 242 |
| abstract_inverted_index.0.909±0.021 | 236 |
| abstract_inverted_index.Conventional | 166 |
| abstract_inverted_index.artificially | 137 |
| abstract_inverted_index.augmentation | 120, 249, 278 |
| abstract_inverted_index.conventional | 262, 290, 317 |
| abstract_inverted_index.demonstrated | 5 |
| abstract_inverted_index.discriminant | 175, 182 |
| abstract_inverted_index.outperformed | 316 |
| abstract_inverted_index.performance. | 165 |
| abstract_inverted_index.precancerous | 63 |
| abstract_inverted_index.respectively | 247 |
| abstract_inverted_index.specificity. | 20 |
| abstract_inverted_index.spectroscopy | 8 |
| abstract_inverted_index.Convolutional | 312 |
| abstract_inverted_index.convolutional | 85 |
| abstract_inverted_index.spectroscopy. | 44, 327 |
| abstract_inverted_index.characteristic | 153 |
| abstract_inverted_index.One-dimensional | 84 |
| abstract_inverted_index.classification. | 94 |
| abstract_inverted_index.one-dimensional | 142 |
| abstract_inverted_index.(p&lt;0.001, | 273 |
| abstract_inverted_index.(p&lt;0.0001, | 254 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5034358596 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I141074466, https://openalex.org/I141945490, https://openalex.org/I4210150180 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.98822645 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |