Dense Net-Driven Multi-Level Feature Representation for Early Pancreatic Cancer Diagnosis in Medical Imaging

Abstract

The diagnosis of pancreatic cancer is one of the most difficult challenges in medical imaging because of the complex morphology of the pancreas, the subtle appearance of the lesions and the heterogeneous nature of the progression of the disease. The proposed method in this study presents a deep learning-based DenseNet model to capture hierarchical features at multiple levels with high-level connectivity from three imaging modalities: computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US) to improve the diagnostic accuracy of diseases. The proposed architecture takes advantage of dense connectivity to maximize feature reusability, improve gradient flow and efficient representation learning at each layer of the network. The model combines densely connected convolutional blocks, adaptive transition layers, and modality-specific preprocessing, which allows it to learn fine-grained local features but also high-level contextual patterns crucial for detecting early-stage pancreatic abnormalities. After conducting extensive experiments on the benchmark and clinical datasets, the DenseNet-based approach has shown a significant improvement in terms of diagnostic accuracy, sensitivity, and specificity compared to the traditional CNN and other state-of-the-art models. The findings demonstrate the model's versatility in processing multi-modal inputs and its promise of assisting radiologists in the early diagnosis of pancreatic cancer, which could lead to enhanced clinical outcomes and a lower mortality rate.