Enhanced VGG 16 Architecture for Pancreatic Tumor Classification Using Attention Mechanism

Pancreatic Tumor classification is a complex task due to challenges such as low contrast and noise in CT scan images, which can affect diagnostic accuracy. This paper presents an Enhanced VGG16 Architecture, known for its effective feature extraction in medical imaging, while integrating advanced techniques to enhance classification performance. The model incorporates multi-head attention, which improves feature recognition by enabling the network to focus on critical tumor regions. Gaussian noise is introduced to enhance generalization by making the model more robust to variations in image quality. Batch normalization and dropout further optimize learning by stabilizing activations and preventing overfitting. By combining these enhancements, the Enhanced VGG16 Architecture ensures precise and reliable tumor classification, addressing key challenges in medical imaging and improving early detection of pancreatic cancer.