Abstract
Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.
Highlights
Digestive diseases are a serious threat to human health
We propose a novel, quick, automatic, accurate, and robust fecal trait examination approach
A valuable fecal trait dataset was collected in a real hospital environment and all the images were labeled by professional doctors
Summary
Digestive diseases are a serious threat to human health. A quick, automatic, accurate, and robust fecal examination approach could greatly reduce the burden on medical inspectors. This type of approach has remained elusive due to the scarcity of datasets and the low accuracy, and time-consuming manual examination methods are still widely used in most hospitals. Medical inspectors need to be close to the feces samples, which leads to a tremendous risk of cross infection. Fecal examinations are highly important in the clinical diagnosis of digestive diseases. If feces samples can be acquired with vision sensors, and Sensors 2020, 20, 2644; doi:10.3390/s20092644 www.mdpi.com/journal/sensors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
