Enhancing Colorectal Polyps Detection using Transfer Learning on DICOM Metadata

Abstract

Colorectal and Rectum Cancer (CRC) presents significant global health challenges, necessitating early detection and precise diagnosis to achieve effective treatment and better patient outcomes. Transfer learning techniques have shown considerable promise, especially in cancer detection. This study presents a CRC prevention system based on a fusion of a pre-trained VGG16 model with dense layers for metadata processing. Experiments were performed using the CT Colonography dataset from The Cancer Imaging Archive (TCIA), applying preprocessing and class weighting to address class imbalance. The system was evaluated using accuracy, loss, recall, precision, F1-score, and AUC. This study investigated the impact of integrating DICOM patient metadata to enhance the proposed CRC prevention system. The findings indicate that the proposed MetaVGGNet model outperformed the standard VGG16, achieving greater accuracy (82%) and a marginally lower loss. This successful application has the potential to enhance CRC diagnosis and treatment and underscores the importance of incorporating metadata into deep learning classification systems, offering avenues for more effective and dependable diagnostic tools in CRC management.