Published on: October 2026
A COMPARATIVE STUDY OF LIGHTWEIGHT AND STANDARD CNN ARCHITECTURES FOR MULTI-CLASS SKIN DISEASE CLASSIFICATION ON DERMOSCOPIC AND CLINICAL IMAGE DATASETS
Manisha Roshini Neethi Koil Pillai
Dr. Abuzar Ansari
SIES College of Arts, Science and Commerce
Article Status
Available Documents
Abstract
Keywords: Skin disease classification, deep learning, EfficientNet, MobileNetV2, ResNet, HAM10000, dermoscopy, clinical images, data cleaning
How to Cite this Paper
Roshini, M. & Pillai, N. K. (2026). A Comparative Study of Lightweight and Standard CNN Architectures for Multi-Class Skin Disease Classification on Dermoscopic and Clinical Image Datasets. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(10), 1-9. https://doi.org/10.55041/ijcope.v2i10.150
Roshini, Manisha, and Neethi Pillai. "A Comparative Study of Lightweight and Standard CNN Architectures for Multi-Class Skin Disease Classification on Dermoscopic and Clinical Image Datasets." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i10.150.
Roshini, Manisha, and Neethi Pillai. "A Comparative Study of Lightweight and Standard CNN Architectures for Multi-Class Skin Disease Classification on Dermoscopic and Clinical Image Datasets." International Journal of Creative and Open Research in Engineering and Management 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i10.150.
Search & Index
References
- 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.
- Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Mach. Learn. (ICML), 2019, pp. 6105–6114.
- Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 4510–4520.
- He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778.
- Esteva et al., “Dermatologist-level classification of skin cancer with deep neural net-works,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.
- Codella et al., “Skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the Interna-tional Skin Imaging Collaboration (ISIC),” in Proc. IEEE 15th Int. Symp. Biomed. Imag-ing (ISBI), 2018, pp. 168–172.
- C. F. Codella et al., “Skin lesion analysis toward melanoma detection 2018: A chal-lenge hosted by the International Skin Imaging Collaboration (ISIC),” arXiv:1902.03368, 2019.
- A. Haenssle et al., “Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists,” Ann. Oncol., vol. 29, no. 8, pp. 1836–1842, 2018.
- J. Brinker et al., “Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task,” Eur. J. Cancer, vol. 113, pp. 47–54, 2019.
- Gessert et al., “Skin lesion classification using ensembles of multi-resolution Efficient-Nets with meta data,” Methods, vol. 179, pp. 1–8, 2020.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Oct 24 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.