Deep Learning for Skin Cancer Classification: A Case Study Using EfficientNetB3

Melanoma is one of the still major worldwide health concerns related to skin cancer; early diagnosis is therefore essential to raise survival rates and patient outcomes. Leveraging the capability of the finely adjusted EfficientNetB3 model, this work focuses on employing deep learning approaches to automatically classify skin lesions as benign or malignant. Extracted from the open-source Kaggle platform was a dataset including 660 photos (with 360 benign and 300 malignant cases). Preprocessing and augmentation methods were used on the photos to improve model generalizing and lower overfitting. With an F1-score of 0.92 for benign lesions, an accuracy of 91% overall, and a precision of 0.93, the refined EfficientNetB3 model attained. The accuracy, recall, and F1-score for malignant lesions were respectively 0.89, 0.91, and 0.90. With little misclassification—especially for malignant cases, which are vital for early intervention—the confusion matrix validated the dependability of the model. These findings show the possibilities of the EfficientNetB3 model in presenting an accurate, dependable, and automated solution for skin cancer detection, so providing an advanced tool that could help doctors in early diagnosis and treatment planning.