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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

MELANOMA CLASSIFICATION ON DERMOSCOPY IMAGES USING A NEURAL NETWORK ENSEMBLE MODEL

Navin S Piragadeesh TM Praveen K Sheik Fayaz Ahamed MS

M. Prabhakaran

Dept. of Electronics and Communication Engineering Chettinad College of Engineering and Technology Karur India
DOI:https://doi.org/10.55041/ijcope.v2i4.861

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Abstract

Melanoma is a very aggressive skin cancer that would need early and correct diagnosis to be treated. Nevertheless, it is not an easy task to distinguish between melanoma and benign lesions since melanoma and benign lesions may look similar re- garding their visual characteristics including color, texture and shape. The present paper introduces an automated melanoma classification system that will be based on an ensemble model based on Convolutional Neural Network (CNN). The suggested system would involve preprocessing methods that include nor- malization, artifact elimination and lesion segmentation to en- hance the quality of the input. The feature extraction is per- formed on multiple CNN models whose outputs are weighted av- eraged to increase the classification accuracy and minimize over- fitting. Experimental outcomes show better performance using the experimental methods over single-model methods, as well as accuracy and robustness. Explainable AI techniques are also in- corporated in the system to aid in clinical decision-making.

 Keywords — Melanoma, Dermoscopy Images, CNN, Ensemble Learning, Deep Learning, Medical Image Analysis.

 

How to Cite this Paper

S, N., TM, P., K, P. & MS, S. F. A. (2026). Melanoma Classification on Dermos Images Using A Neural Network Ensemble Model. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.861

S, Navin, et al.. "Melanoma Classification on Dermos Images Using A Neural Network Ensemble Model." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.861.

S, Navin,Piragadeesh TM,Praveen K, and Sheik MS. "Melanoma Classification on Dermos Images Using A Neural Network Ensemble Model." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.861.

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References


  • Esteva, B. Kuprel, R. A. Novoa, et al., “Dermatologist- level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.

  • Codella, D. Gutman, M. Celebi, et al., “Skin lesion anal- ysis toward melanoma detection: A challenge at the 2017 ISIC,” in Proc. IEEE Int. Symp. Biomed. Imaging (ISBI), 2018, pp. 168–172.

  • Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset: A large collection of dermatoscopic im- ages,” Sci. Data, vol. 5, 2018.

  • Combalia, N. Codella, V. Rotemberg, et al., “BCN20000:Dermoscopic lesions dataset,”arXiv:1908.02288, 2019.

  • Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Mach. Learn. (ICML), 2019.

  • He, X. Zhang, S. Ren, and J. Sun, “Deep residual learn- ing for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778.

  • Szegedy, W. Liu, Y. Jia, et al., “Going deeper with con- volutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog- nit. (CVPR), 2015, pp. 1–9.

  • Huang, Z. Liu, L. Van Der Maaten, and K. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 4700–4708.

  • Yu, J. Zhang, and Y. Xia, “Automated melanoma recog- nition in dermoscopy images via deep learning,” IEEE Access, vol. 8, pp. 208354–208364, 2020.

  • J. Brinker, A. Hekler, A. H. Enk, et al., “Deep learning for skin cancer detection: A systematic review,” Eur. J. Can- cer, vol. 120, pp. 103–112, 2019.

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  • •Published on: Apr 29 2026
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