PortableNet: An Automatical Diagnosis Method for Gastrointestinal Motility Analysis

Abstract Gastrointestinal motility serves as a crucial diagnostic indicator for functional gastric disorders. However, manual analysis of swallowing cloud images in gastrointestinal motility presents numerous drawbacks, including time-intensive processes, labor-intensive procedures, and subjectivity in diagnosis. In response to these challenges, we propose a gastrointestinal motility analysis model based on deep learning techniques to facilitate preliminary diagnosis of swallowing cloud images in gastrointestinal motility, thereby augmenting diagnostic efficiency for medical practitioners. Initially, we construct a gastrointestinal motility dataset. Subsequently, we introduce a light weight model, termed PortableNet, derived from MobileNet architecture. The research findings demonstrate that the PortableNet model achieves a recognition accuracy of 93.99% with only 4.3 million parameters. Compared to the MobileNet architecture model, there is a 5.05% increase in recognition accuracy and a 58.65% reduction in size. These results collectively indicate that the PortableNet model delivers superior overall performance, offering both high accuracy and efficiency in recognizing gastrointestinal motility swallowing cloud images. Such advancements can significantly enhance diagnostic efficiency among medical professionals, thereby offering valuable insights for future research endeavors.