Ensemble Deep Learning Framework for Skin Cancer Classification

Skin cancer remains a significant global health issue, and improving patient outcomes and treatment success relies heavily on early and accurate detection. To enhance the accuracy of skin cancer classification, this study presents an ensemble deep learning architecture that combines the strengths of multiple deep learning models. The suggested system extracts different and complimentary information from dermoscopic images using a combination of advanced convolutional neural networks (CNNs) such as ResNet50, DenseNet121, and EfficientNetB0. Each model is uniquely trained and fine-tuned on ISIC 2020 dataset, a widely recognized benchmark for skin cancer classification. By merging these models, we aim to leverage their distinct feature extraction strengths and boost overall classification accuracy. The ensemble approach employs a weighted voting mechanism to combine the predictions from all models, reducing the risk of overfitting and enhancing the robustness and generalization of the classification system. To address issues related to dataset imbalance and further improve model performance, we utilize advanced techniques like transfer learning, fine-tuning, and data augmentation. Extensive testing on the ISIC 2020 dataset demonstrates the effectiveness of our ensemble framework. Our model achieves remarkable results, with 95.2% accuracy, 93.5% sensitivity, and 96.1% specificity. These metrics reflect model’s potential to accurately differentiate between benign and malignant skin lesions. Furthermore, our system incorporates attention mechanisms to improve interpretability and diagnostic transparency, aiding physicians in understanding model’s analytical approach. This feature is pivotal to gain clinical trust and acceptance of automated diagnostic tools.