A Hybrid Approach Combining CNN and Ensemble Algorithms for Dermoscopic Image Classification

• Pedro A. A. Soares Centro Universitário FAG
• Leandro A. Ensina UTFPR
• Juliano H. Foleis UTFPR

DOI: https://doi.org/10.5753/eniac.2025.12460

Abstract

This work presents a hybrid approach for the classification of pigmented skin lesions, including skin cancer types, in dermoscopic images. The technique combines convolutional neural networks (CNNs) as feature extractors and ensemble algorithms as classifiers, along with the introduction of two distinct preprocessing stages for data augmentation. The first stage occurs before training the CNNs, while the second is applied prior to training the classifiers. Experiments conducted with the HAM10000 dataset demonstrate the method’s effectiveness, achieving overall F1-Scores above 80%. Additionally, the study suggests directions for future work aimed at improving the method.

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