Advancement of Lung Cancer Diagnosis with Transfer Learning: Insights from VGG16 Implementation
Abstract
Lung cancer continues to be one of the leading causes of cancer-related mortality globally, largely due to the challenges associated with its early and accurate detection. Timely diagnosis is critical for improving survival rates, and advances in artificial intelligence (AI), particularly deep learning, are proving to be valuable tools in this area. This study introduces an enhanced deep learning-based approach for lung cancer classification using the VGG16 neural network architecture. While previous research has demonstrated the effectiveness of ResNet-50 in this domain, the proposed method leverages the strengths of VGG16 particularly its deep architecture and robust feature extraction capabilities to improve diagnostic performance. To address the limitations posed by scarce labelled medical imaging data, the model incorporates transfer learning and fine-tuning techniques. It was trained and validated on a well-curated dataset of lung CT images. The VGG16 model achieved a high training accuracy of 99.09% and a strong validation accuracy of 95.41%, indicating its ability to generalize well across diverse image samples. These results reflect the model’s capacity to capture intricate patterns and subtle features within medical imagery, which are often critical for accurate disease classification. A comparative evaluation between VGG16 and ResNet-50 reveals that VGG16 outperforms its predecessor in terms of both accuracy and reliability. The improved performance underscores the potential of the proposed approach as a reliable and scalable AI-driven diagnostic solution. Overall, this research highlights the growing role of deep learning in enhancing clinical decision-making, offering a promising path toward earlier detection of lung cancer and ultimately contributing to better patient outcomes.
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