Attention-Guided Ensemble Deep Learning Model for Skin Cancer Detection Using Lesion Images
Keywords:
Deep learning, DenseNet121, Dermoscopic images, EfficientNet-B4, Ensemble learning, Medical image classification, Skin cancer detection, Vision transformerAbstract
Due to its high prevalence and the rapid progression of its malignant forms, skin cancer is a critical public health issue where rapid and accurate detection significantly influences survival rates. Currently, diagnosing this disease primarily relies on a dermatologist's visual assessment, followed by histopathological confirmation. This traditional method is inherently time-consuming, requires significant specialist resources, and is susceptible to inter-observer variability. To address these challenges, the study introduces a novel attention-guided ensemble deep learning framework designed to automate lesion classification directly from dermoscopic imagery. This study system synergistically combines EfficientNet-B4, DenseNet121, and a Vision Transformer (ViT) into a unified architecture. This integration ensures that both localized textural details and macro-level contextual structures are comprehensively analyzed during the final classification phase, preventing the loss of crucial diagnostic information. The complete workflow encompasses image preprocessing, lesion segmentation, multi-backbone feature extraction, ensemble fusion, and rigorous performance evaluation. The model was trained and evaluated using the ISIC dermoscopic dataset, comprising 2,357 benign and malignant cases. The study's ensemble approach achieved an outstanding accuracy of 99.21%, outperforming standard architectures such as ResNet50, ResNet101, and ResNet152 across all key metrics, including precision, recall, and F1-score. These findings strongly indicate that employing ensemble deep learning strategies offers a substantial improvement over single-model architectures, presenting a highly viable tool for computer-aided diagnosis in dermatology.
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