Multimodal deep learning ensemble framework for skin cancer detection
Open accessAbstract
Skin cancer is the abnormal growth of skin cells, most often developing on skin exposed to the sun. It is among the most fatal forms of cancer, making its early detection and therapy crucial. In addition to conventional techniques, deep learning methods are increasingly utilized for accurate identification and classification. This study proposes a convolutional neural network (CNN) model using transfer learning to detect and classify multiple types of skin cancer. This study focuses on evaluating the efficacy of transfer learning techniques in enhancing CNN performance for this critical task. A significant contribution of this study is the use of transfer learning to improve CNN performance in skin cancer detection and classification by leveraging pre-trained models, including ResNet50, Xception, MobileNet, EfficientNetB0, and DenseNet121. The integration of metadata demonstrated a significant improvement in accuracy compared to using images alone, enhancing the performance of most models. Further enhancement was achieved through ensemble techniques, specifically an adaptive weighted ensemble method, which dynamically assigns weights to individual models based on their performance, resulting in superior overall accuracy. SMOTE was used as an oversampling technique to address class imbalance. The proposed fusion of pre-trained models (ResNet50, Xception, and EfficientNetB0) combined with metadata achieved 93.2% accuracy, 93% precision, 93% recall, 93% F1 score, and 97.3% AUC on the ISIC 2018 dataset. On the ISIC 2019 dataset, it achieved 91.1% accuracy, 92% precision, 93% recall, 92% F1 score, and 95.5% AUC, surpassing many state-of-the-art methods. Experiments on an external dataset, Derm7pt, resulted in 82.5% accuracy, with a precision of 86%, recall of 83%, F1 score of 84% and AUC of 89.15%, demonstrating the improved interpretability and generalization of the proposed model. The proposed ensemble model optimizes deep learning for healthcare applications, enhancing dermatological diagnosis and treatment strategies for skin cancer patients.
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Patient metadata provides complementary information that can support skin lesion classification when combined with image features.
“These studies suggest that patient metadata provides complementary information that can support skin lesion classification when combined with image features.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [28]
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A Framework for Skin Cancer Classification using Ensemble Transfer Learning and Data Fusion
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- Authors
- Saeed, M. A., Afify, Y. M., Badr, N. L., & Helal, N. A.
- Affiliations
- Ain Shams University (EG)
- Corresponding author
- Saeed, Mayar Ashraf
- Venue
- Scientific Reports
- Published
- 2025-12-30
- Volume
- 15
- Issue
- 1
- Pages
- 45660-45660
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC12753777
- OpenAlex
- W7117537301
- Cited by
- 7
- References
- 40
- Citations by year
- 2026: 7
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ain Shams University
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- skin cancer detection, skin cancer classification, transfer learning, convolutional neural networks, multimodal fusion, image metadata, SMOTE, class imbalance, pre-trained models, ISIC 2018, ISIC 2019, dermatological diagnosis
- MeSH terms
- Deep Learning, Humans, Skin Neoplasms, Neural Networks, Computer
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