Deep Learning's Role in Advancing Gastroenterology and Digestive Health
Ramgopal Kashyap, Vandana Roy, Premsagar D. Patil, Advin Manhar, Lipika Roy
Abstract
This work presents a comprehensive methodology for the application of deep learning in the field of gastroenterology and digestive health. The methodology emphasizes the crucial steps involved in harnessing the power of deep learning, commencing with data preprocessing. Data cleaning, normalization, and partitioning into training, validation, and test sets lay the foundation for subsequent analysis. The choice of deep learning algorithms is pivotal, aligning them with the specific problem at hand. The results generated by these models hold immense promise, spanning accurate polyp detection, disease prediction, and unveiling microbiome patterns, all of which are translated into actionable insights for gastroenterologists and healthcare providers. Moreover, ethical, and regulatory considerations feature prominently, addressing patient data privacy and compliance with healthcare regulations. The proposed methodology offers a robust framework for the integration of deep learning in gastroenterology, promising to advance our understanding of digestive health, improve patient care, and drive medical innovation.