PortableNet: An Automatical Diagnosis Method for Esophageal Motility Disorders

The detection of esophageal motility disorders serves as a crucial diagnostic indicator for preventing esophageal diseases. Currently, the swallowing cloud image obtained by high-resolution esophageal manometry is mainly used for esophageal motility analysis; however, manual analysis of swallowing cloud images presents numerous drawbacks, including time-intensive processes, labor-intensive procedures, and subjectivity in diagnosis. In response to these challenges, we propose an esophageal motility analysis model based on deep learning techniques to facilitate preliminary diagnosis of swallowing cloud images, thereby augmenting diagnostic efficiency for medical practitioners. Initially, we construct an esophageal 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. In terms of classification performance, the model also achieves a precision of 94.73%, a recall of 93.39%, and an F1-score of 94.06%. 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 esophageal motility swallowing cloud images. Such advancements can significantly enhance diagnostic efficiency among medical professionals, thereby offering valuable insights for future research endeavors.