Transformative advances in lung cancer diagnosis: Deep learning-based classification using innovative image augmentation techniques

Lung cancer, the leading cause of cancer-related deaths globally, emphasizes the critical necessity for early detection and accurate classification to ensure effective treatment. This study employs innovative image augmentation techniques and incorporates architectural advancements to provide a unique deep learning-based solution for lung cancer classification. Utilizing a diverse dataset encompassing benign, malignant, and normal cases, the research meticulously preprocesses images and fine-tunes the EfficientNetB7 model to address the complexities of lung nodule classification. Visualizations such as training/validation curves illuminate the model's learning dynamics, underscoring its resilience and generalization capability. Notably, the integration of image augmentation significantly enhances the model's performance and adaptability.