Next-Gen Dermatology: Effective Skin Lesion Classification Using EfficientNetV2B0 for Early Detection of Cancer

A common condition needing early identification is skin cancer; this work uses EfficientNetV2B0 for precise lesion classification.The practical application of the EfficientNetV2B0 model for the classification of skin lesions into benign and malignant categories is thoroughly investigated in this research work. Carefully selected to offer a balanced portrayal of both classes, the dataset used in this study consists of high-resolution photographs of skin lesions. Using its compound scaling approach to reach state-of-the-art performance with less parameters than conventional models, the EfficientNetV2B0 architecture was chosen for its demonstrated efficiency and accuracy in image classification applications. Over several epochs, the training process produced an outstanding training accuracy of 96%, which matched a validation accuracy of also $\mathbf{9 6 \%}$. With an accuracy of 1.00 for benign lesions and 95% for malignant lesions, together with a perfect recall of $\mathbf{1 0 0 \%}$ for malignant instances, the precision and recall measures confirmed even more the efficacy of the model. These findings highlight the great possibility of the model for practical uses in dermatology since they provide a consistent instrument for early skin cancer diagnosis and identification. The results imply that the EfficientNetV2B0 model is not only efficient but also quite effective, which opens the path for more investigation on best machine learning approaches for medical image interpretation.