DeepSkinNet: An EfficientNetV2B0-Based Framework for Binary Skin Lesion Classification
Article Sidebar
Main Article Content
Abstract
The classification of skin lesions with deep learning is still difficult when there is a limited amount of labeled dermoscopic data. In this work, the authors propose an EfficientNetV2B0 framework for binary classification of actinic keratosis (AKIEC) and vascular lesions (VASC), which is referred to as DeepSkinNet. Images that were verified for this study were 270 images, which included 128 AKIEC images and 142 VASC images from the HAM10000 dataset. To avoid information leakage from multiple images in one dataset belonging to the same lesion, the datasets were split on a lesion-by-lesion basis using the HAM10000 lesion identifiers with the aim that no specific lesion would be present across training, validation, and test sets. The framework features two-stage selective fine-tuning, online data augmentation, class weighting, Test-Time Augmentation (TTA), and a decision threshold optimized for the validation set, as well as transfer learning with ImageNet. The optimized threshold (0.59) is solely based on the validation set and further applied to the independent test set. DeepSkinNet's performance on the lesion independent test set (44 images) was 93.18% accuracy, 92.86% balanced accuracy, 93.88% F1-score and 99.79% ROC-AUC. The findings in this study show that discrimination between AKIEC and VASC was promising under the protocol for the evaluation of leakage, and further validation on larger and external datasets is needed.
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Deprecated: json_decode(): Passing null to parameter #1 ($json) of type string is deprecated in /home/u273879158/domains/mesopotamian.press/public_html/journals/plugins/generic/citations/CitationsPlugin.php on line 68
How to Cite
References
[1] P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset: A large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific Data, vol. 5, Art. no. 180161, 2018, doi: 10.1038/sdata.2018.161.
[2] A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017, doi: 10.1038/nature21056.
[3] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the 36th International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 6105–6114.
[4] M. Tan and Q. V. Le, “EfficientNetV2: Smaller models and faster training,” in Proceedings of the 38th International Conference on Machine Learning (ICML), vol. 139, 2021, pp. 10096–10106.
[5] O. Russakovsky et al., “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.
[6] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778, doi: 10.1109/CVPR.2016.90.
[7] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4700–4708, doi: 10.1109/CVPR.2017.243.
[8] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520.
[9] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2015.
[10] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no. 56, pp. 1929–1958, 2014.
[11] K. Nakai, Y.-W. Chen, and X.-H. Han, “Enhanced deep bottleneck transformer model for skin lesion classification,” Biomedical Signal Processing and Control, vol. 78, Art. no. 103997, 2022, doi: 10.1016/j.bspc.2022.103997.
[12] A. T. Ibrahim, M. Abdullahi, A. F. D. Kana, M. T. Mohammed, and I. H. Hassan, “Categorical classification of skin cancer using a weighted ensemble of transfer learning with test-time augmentation,” Data Science and Management, vol. 8, no. 2, pp. 174–184, 2025, doi: 10.1016/j.dsm.2024.10.002.
[13] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618–626, doi: 10.1109/ICCV.2017.74.
[14] Z. Hu, W. Mei, H. Chen, and W. Hou, “Multi-scale feature fusion and class weight loss for skin lesion classification,” Computers in Biology and Medicine, vol. 176, Art. no. 108594, 2024, doi: 10.1016/j.compbiomed.2024.108594.
[15] K. Behara, E. Bhero, and J. T. Agee, “An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks,” Diagnostics, vol. 14, no. 6, Art. no. 636, 2024, doi: 10.3390/diagnostics14060636.
[16] K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer,” Neuroscience Informatics, vol. 2, no. 4, p. 100034, 2022, doi: 10.1016/j.neuri.2021.100034.
[17] S. Qian, K. Ren, W. Zhang, and H. Ning, “Skin lesion classification using CNNs with grouping of multi-scale attention and class-specific loss weighting,” Computer Methods and Programs in Biomedicine, vol. 226, p. 107166, 2022, doi: 10.1016/j.cmpb.2022.107166.
[18] J.-Y. Choi, M.-J. Song, and Y.-J. Shin, “Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model,” Applied Sciences, vol. 14, no. 22, Art. no. 10294, 2024, doi: 10.3390/app142210294.
[19] S. Das and R. K. Addya, “Skin Cancer Detection and Classification Through Medical Image Analysis Using EfficientNet,” NDT, vol. 3, no. 4, article 23, 2025, doi: 10.3390/ndt3040023.