Skin Lesion Classification Using a Soft Voting Ensemble of Convolutional Neural Networks

Skin cancer can be identified through dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), leveraging annotated skin images and Convolutional Neural Networks (CNNs), enhances diagnostic accuracy. This paper presents an early skin cancer classification method using a soft voting ensemble of CNNs. Three benchmark datasets—HAM10000, ISIC 2016, and ISIC 2019—were used in this research. The process involved rebalancing, image augmentation, and filtering techniques, followed by a hybrid dual encoder for segmentation via transfer learning. Accurate segmentation focused classification models on clinically significant features, reducing background artifacts and improving accuracy. Classification was performed through an ensemble of MobileNetV2, VGG19, and InceptionV3, balancing accuracy and speed for real-world deployment. The method achieved lesion recognition accuracies of 96.32%, 90.86%, and 93.92% for the three datasets. The system’s performance was evaluated using established skin lesion detection metrics, yielding impressive results.