Enhanced skin cancer diagnosis via deep convolutional neural networks with ensemble learning
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions.
“However, most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions (14–22).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [21]
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References 37 · Cited by 21
- Authors
- Khan, M. A., Alam, S., & Ahmed, W.
- Affiliations
- Jamia Millia Islamia (IN)
- Corresponding author
- Khan, Mohd Anas
- Venue
- SN Computer Science
- Published
- 2025-01-30
- Volume
- 6
- Issue
- 2
- ISSN
- 2661-8907 (ISSN-L)
- OpenAlex
- W4407008438
- Cited by
- 21
- References
- 37
- Citations by year
- 2026: 11 · 2025: 10
- FWCI
- 8.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- SN Computer Science (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- skin cancer classification, computer-aided diagnosis, dermoscopic images, ensemble learning, deep convolutional neural networks, InceptionV3, HAM10000 dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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