A robust skin cancer classification using deep learning
Open accessAbstract
In the medical field, detecting skin cancer from pictures can be difficult. Skin cancer identification is a laborious process in modern medicine that could eventually result in patient death. Early detection of skin cancer is essential to the effectiveness of full treatment. It is difficult to diagnose skin cancer effectively. Consequently, there are not enough qualified dermatologists around the globe to meet the demands of modern healthcare. Data imbalance problems are caused by the wide diversity in data from several healthcare industry categories. Deep learning models are frequently trained in one category more than others due to issues with data imbalance. Using an unbalanced dataset, this study suggests a revolutionary deep learning-based skin cancer detector. Data augmentation was used to balance different skin cancer categories and overcome data imbalance. The MNIST: HAM 10000 skin cancer dataset, consisting of seven skin lesion categories, was used. Deep learning models are widely used in image-based disease diagnosis. Deep learning-based models (Custom CNN, DenseNet201, ResNet-101, and Xception) have been used to classify skin cancer. The proposed framework was fine-tuned using different sets of hyperparameters. The results show that Xception outperformed ResNet-101, DenseNet201, and Custom CNN in terms of accuracy, Fl score, and receiver operating characteristic (ROC) curve. It achieved an accuracy of 0.9148. Our proposed framework could help in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare workers.
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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 [18]
Later, CNN-based automatic feature extraction and transfer learning with pretrained models improved representation learning.
“Later, CNN-based automatic feature extraction (14) and transfer learning with pretrained models (15, 18) improved representation learning.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [18]
Most prior studies relied primarily on the fully connected layer for final classification.
“In the classification stage, most prior studies relied primarily on the fully connected layer for final classification (7, 14, 16–18, 20).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [18]
Cited by
Sorted by citations
Alzakari, S. A., Alhussan, A. A., Towfek, S. K., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12050478 · Cited by 5 · see citing works
“Researchers published recent findings about a deep convolutional neural network (CNN) framework that produced exceptional results in robust skin cancer classification and analysis on dermoscopic images according to study findings presented in [58].”
Sonuç, E., Saihood, Q., Yargı Baydilli, Y., & Özcan, E. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1871689
Elbehiery, H., Fouad, M. M., El-Sisi, F. N., et al. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15784-3_29
Self-citation
- Authors
- Abbas, A., & Elbehiery, H.
- Affiliations
- October 6 University (EG)
- ORCID
- Elbehiery, Hussam
- Venue
- Sustainable Machine Intelligence Journal
- Published
- 2025-03-10
- Volume
- 10
- Pages
- 86-95
- ISSN
- 3005-3609 (ISSN-L)
- 3005-3617
- OpenAlex
- W4408314357
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Sustainable Machine Intelligence Journal (publishedVersion, cc-by)
- 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 classification, skin lesion images, HAM10000 dataset, class imbalance, data augmentation, Xception, DenseNet201, ResNet-101, custom CNN, early cancer detection, dermatology
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