DCENSnet: A new deep convolutional ensemble network for skin cancer classification

Abstract

Skin cancer is a significant health concern, demanding early detection and classification for higher survival rates. Existing computer vision methods struggle to tackle fine-grained variability in skin lesion features across different surfaces. Deep Convolutional Neural Network (DCNN) models show promise, but their current ad-hoc developments overlook redundant layers and suffer from imbalanced datasets and inadequate augmentation. To address these problems, we propose a novel ensemble approach involving three DCNNs, each customized with different dropout layers to enhance feature-level learning. Thus, the proposed ensemble network that we call DCENSnet achieves a superior bias–variance trade-off. Evaluating on the popular HAM10000 skin lesion dataset, our model outperforms state-of-the-art networks, achieving a mean accuracy of 99.53% along with high precision, recall, F1 score, and Area Under the ROC Curve (AUC) for each class. This method proves highly reliable for computer-aided detection, classification, and analysis of malignant skin lesions, holding promise for improving diagnosis and treatment accuracy.