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Updated: Oct 1, 2025

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

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Published on: April 25, 2025

A computer-aid multi-task light-weight network for macroscopic feces diagnosis.

Ziyuan Yang^1,2, Lu Leng^1,3, Ming Li⁴

¹School of Software, Nanchang Hangkong University, Nanchang, 330063 People's Republic of China.

Multimedia Tools and Applications

|March 7, 2022

Summary

This study introduces a novel multi-task network for analyzing feces images, improving diagnostic accuracy for digestive diseases. The system uses data augmentation and over-sampling to address rare pathological samples, enhancing detection speed and reducing infection risk.

Keywords:

Color recognition Data augmentation, over-sampling Macroscopic feces image Multi-task diagnosing Trait recognition Weighting selection

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Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Gastroenterology

Background:

Abnormal fecal traits and colors often signal tumors or digestive diseases.
Current diagnostic methods can be slow and increase infection risk.
Pathological image rarity challenges the accuracy of automated diagnostic models.

Purpose of the Study:

To develop a fast, accurate, and automatic health diagnosis system using feces analysis.
To improve the recognition performance of pathological feces images.
To leverage correlations between feces traits and colors for enhanced diagnosis.

Main Methods:

Employed data augmentation and over-sampling to address limited pathological samples.
Developed a multi-task network to simultaneously recognize feces colors and traits.
Implemented a weighted loss function with task difficulty determined by fitted linear functions.

Main Results:

The multi-task network achieved higher accuracies compared to single-task models.
The proposed method demonstrated improved diagnostic efficiency.
Reduced storage costs due to a smaller parameter count in the multi-task network.

Conclusions:

The developed multi-task network effectively analyzes macroscopic feces images for disease diagnosis.
Data augmentation and intelligent weighting strategies enhance diagnostic accuracy and efficiency.
This approach offers a promising solution for rapid and safe health assessment via feces analysis.