Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Augmenting Gastrointestinal Health: A Deep Learning Approach to Human
  Stool Recognition and Characterization in Macroscopic Images   Purpose - Functional bowel diseases, including irritable bowel syndrome,
chronic constipation, and chronic diarrhea, are some of the most common
diseases seen in clinical practice. Many patients describe a range of triggers
for altered bowel consistency and symptoms. However, characterization of the
relationship between symptom triggers using bowel diaries is hampered by poor
compliance and lack of objective stool consistency measurements. We sought to
develop a stool detection and tracking system using computer vision and deep
convolutional neural networks (CNN) that could be used by patients, providers,
and researchers in the assessment of chronic gastrointestinal (GI) disease.

15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
19
The categories of this paper are: eess
3
The categories of this paper are: physics