A Light-Weight Practical Framework for Feces Detection and Trait Recognition

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.

Detection and Classification of Human Stool Using Deep Convolutional Neural Networks

“…To the extent of our knowledge, a few works [20], [29] obtained their fully labeled stool image dataset according to the BSFS, but they were unavailable at the time of writing. The other publicly available stool image datasets compiled by Leng et al [28] and Yang et al [30] were incompatible with our study because the sample images were not classified using the BSFS. In this manner, we attempted to collect and compile our dataset for research purposes that adhered to the BSFS classification scheme.…”

“…The annotation and labeling of the stool images often require a significant amount of effort and time. Furthermore, few patients were willing to provide their stool samples due to shameful and embarrassing reasons [27], [28]. Hence, the shortage of labeled stool image datasets is currently an impediment to innovation in this field.…”

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