Melanoma Stage Classification Based on Hybrid Heterogeneous Multi-Classifier Ensemble Learning


  • Shweta M. Dept of ISE, PDA College of Engineering, Kalaburagi
  • Vishwanath Burkpalli HOD and Professor, Dept of ISE, PDA College of Engineering, Kalaburagi


melanoma detection, skin cancer, machine learning, multiclass classifier, ensemble learning


Background: Melanoma is one of the most dangerous types of skin cancer, and it can be fatal if it is not detected at initial stage. Therefore, melanoma detection requires a precise diagnosis.

Objective: To build Hybrid Heterogeneous Multi-Classifier Ensemble learning models to classify and identify skin cancer.

Methods: Models that help make skin cancer predictions more accurate are built using a model-driven framework in the cloud that uses machine learning (ML) methods at its core. The study shows how to make models and use them to put skin tumors into groups.

Results: Hybrid Heterogeneous Multi-Classifier Ensemble Learning models built here are tested on ISIC2019 dataset, and accuracy of 95.10% was observed.

Conclusions: A practitioner may easily construct the hybrid ensemble machine learning models to predict skin cancer using the model-driven architecture. The suggested model can also find photographs that don't fit into any of the three classifications.


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How to Cite

M., S. ., & Burkpalli, V. . (2023). Melanoma Stage Classification Based on Hybrid Heterogeneous Multi-Classifier Ensemble Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 27–39. Retrieved from



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