Skin Lesion Classification Through Test Time Augmentation and Explainable Artificial Intelligence Article Swipe
Loris Cino
,
Cosimo Distante
,
Alessandro Martella
,
Pier Luigi Mazzeo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jimaging11010015
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jimaging11010015
Despite significant advancements in the automatic classification of skin lesions using artificial intelligence (AI) algorithms, skepticism among physicians persists. This reluctance is primarily due to the lack of transparency and explainability inherent in these models, which hinders their widespread acceptance in clinical settings. The primary objective of this study is to develop a highly accurate AI-based algorithm for skin lesion classification that also provides visual explanations to foster trust and confidence in these novel diagnostic tools. By improving transparency, the study seeks to contribute to earlier and more reliable diagnoses. Additionally, the research investigates the impact of Test Time Augmentation (TTA) on the performance of six Convolutional Neural Network (CNN) architectures, which include models from the EfficientNet, ResNet (Residual Network), and ResNeXt (an enhanced variant of ResNet) families. To improve the interpretability of the models’ decision-making processes, techniques such as t-distributed Stochastic Neighbor Embedding (t-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) are employed. t-SNE is utilized to visualize the high-dimensional latent features of the CNNs in a two-dimensional space, providing insights into how the models group different skin lesion classes. Grad-CAM is used to generate heatmaps that highlight the regions of input images that influence the model’s predictions. Our findings reveal that Test Time Augmentation enhances the balanced multi-class accuracy of CNN models by up to 0.3%, achieving a balanced accuracy rate of 97.58% on the International Skin Imaging Collaboration (ISIC 2019) dataset. This performance is comparable to, or marginally better than, more complex approaches such as Vision Transformers (ViTs), demonstrating the efficacy of our methodology.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jimaging11010015
- https://www.mdpi.com/2313-433X/11/1/15/pdf?version=1736470072
- OA Status
- gold
- Cited By
- 5
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406216827
All OpenAlex metadata
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https://openalex.org/W4406216827Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jimaging11010015Digital Object Identifier
- Title
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Skin Lesion Classification Through Test Time Augmentation and Explainable Artificial IntelligenceWork 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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2025-01-09Full publication date if available
- Authors
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Loris Cino, Cosimo Distante, Alessandro Martella, Pier Luigi MazzeoList of authors in order
- Landing page
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https://doi.org/10.3390/jimaging11010015Publisher landing page
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https://www.mdpi.com/2313-433X/11/1/15/pdf?version=1736470072Direct link to full text PDF
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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://www.mdpi.com/2313-433X/11/1/15/pdf?version=1736470072Direct OA link when available
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Artificial intelligence, Computer science, Test (biology), Pattern recognition (psychology), Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 75 |
| abstract_inverted_index.a | 52, 166, 218 |
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| abstract_inverted_index.as | 139, 246 |
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| abstract_inverted_index.is | 21, 49, 154, 181, 235 |
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| abstract_inverted_index.(an | 122 |
| abstract_inverted_index.CNN | 211 |
| abstract_inverted_index.Our | 198 |
| abstract_inverted_index.The | 43 |
| abstract_inverted_index.and | 29, 69, 86, 120, 145 |
| abstract_inverted_index.are | 151 |
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| abstract_inverted_index.skin | 8, 58, 177 |
| abstract_inverted_index.such | 138, 245 |
| abstract_inverted_index.that | 61, 186, 193, 201 |
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| abstract_inverted_index.2019) | 231 |
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| abstract_inverted_index.their | 37 |
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| abstract_inverted_index.better | 240 |
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| abstract_inverted_index.lesion | 59, 178 |
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| abstract_inverted_index.reveal | 200 |
| abstract_inverted_index.space, | 168 |
| abstract_inverted_index.tools. | 75 |
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| abstract_inverted_index.(ViTs), | 249 |
| abstract_inverted_index.(t-SNE) | 144 |
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| abstract_inverted_index.Mapping | 149 |
| abstract_inverted_index.Network | 108 |
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| abstract_inverted_index.ResNet) | 126 |
| abstract_inverted_index.complex | 243 |
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| abstract_inverted_index.earlier | 85 |
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| abstract_inverted_index.accuracy | 209, 220 |
| abstract_inverted_index.accurate | 54 |
| abstract_inverted_index.balanced | 207, 219 |
| abstract_inverted_index.classes. | 179 |
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| abstract_inverted_index.dataset. | 232 |
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| abstract_inverted_index.intelligence | 12 |
| abstract_inverted_index.investigates | 93 |
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| abstract_inverted_index.Convolutional | 106 |
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| abstract_inverted_index.t-distributed | 140 |
| abstract_inverted_index.transparency, | 78 |
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| corresponding_institution_ids | https://openalex.org/I861853513 |
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