Detection and Classification of Human Stool Using Deep Convolutional Neural Networks

Abstract

The diagnosis of functional gastrointestinal disorders and chronic digestive system diseases such as irritable bowel syndrome relies heavily on macroscopic examination of human stool specimens. However, traditional manual stool analysis processes are time-consuming and prone to human subjectivity errors that may lead to incorrect judgments. In this study, we employed deep convolutional neural networks (CNN) to automatically recognize and classify stools in macroscopic images. This approach is advantageous because it reduces the amount of direct interaction required by patients or medical staff, lowers the risk of cross-infection, and removes subjectivity in stool analysis. The U-Net segmentation model is applied to define the region of stools by generating a mask image. After combining the mask and the corresponding original image, the ResNeXt-50 classifies the pre-processed stool image according to the modified Bristol Stool Form Scale (BSFS). Overall, the U-Net model yielded a mean intersection-over-union (mIoU) of 93.75% and an F-score of 0.9570, as the ResNeXt-50 classifier had a classification accuracy of 94.35% and showed decent performance in terms of predictive power. Our study may help to improve the quality of diagnosis and monitoring of diseases associated with bowel movement habits by providing reliable measurements in terms of stool form and consistency.

Keywords

• Bristol stool form scale
• convolutional neural network
• deep learning
• functional bowel disease
• stool classification
• stool segmentation