Advanced Skin Cancer Classification using EfficientNetB1: Enhancing Diagnosis and Interpretability

Skin cancer is a major global health concern, and early detection is crucial for effective treatment. This research presents a novel approach to skin cancer classification utilizing the EfficientNetB1 architecture, a state-of-the-art deep learning model. By addressing limitations in existing models, such as limited generalization and interpretability, our approach incorporates data augmentation, transfer learning, and rigorous evaluation. The primary objective is to improve classification performance for various skin lesions while maintaining computational efficiency. The study involves preprocessing a large dataset of skin lesion images, followed by fine-tuning the EfficientNetB1 model. The model achieves an impressive overall test accuracy of 93.15%, demonstrating significant improvements in classification accuracy compared to previous methods. Evaluation metrics, including precision, recall, F1score, and Cohen’s kappa score, further validate the model’s performance. The use of EfficientNetB1 and extensive data augmentation contributes to enhanced accuracy and reduced overfitting. The proposed model offers a robust solution for skin cancer classification with potential applications in clinical settings. Future research will focus on integrating the model into practical diagnostic tools, expanding the dataset, and exploring advanced interpretability methods to further improve clinical decision-making.