The article presents a method for diagnosing intestinal parasitic diseases of dogs using artificial neural networks. For microscopy, an optical microscope is used using an ocular digital USB camera and an electronic computer with the Unix (Linux) operating system. Identification of causative agents of intestinal parasitic diseases of dogs is carried out by an artificial neural network based on the YOLO v5 architecture.

1.
F.
Abdurahman
,
K. F.
Anlay
and
M.
Aliy
,
Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models
,
Research Square Platform LLC
(
2020
).
2.
F.
Abdurahman
,
K. A.
Fante
and
M.
Aliy
,
Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models
,
BMC Bioinformatics
22
,
1
(
2021
).
3.
N.
Butploy
and
W.
Kanarkard
,
Maleewong
Intapan P.
Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification
,
Journal of Parasitology Research
,
1
8
(
2021
).
4.
M.
Górriz
,
Leishmaniasis Parasite Segmentation and Classification Using Deep Learning, Articulated Motion and Deformable Objects
(
Springer International Publishing
,
Cham
,
2018
), pp.
53
62
.
5.
L. E.
Holz
,
D.
Fernandez-Ruiz
and
W. R.
Heath
,
Protective immunity to liver-stage malaria
,
Clinical & Translational Immunology
5
,
10
,
105
(
2016
).
6.
H.
Jiang
,
Geometry-Aware Cell Detection with Deep Learning
,
mSystems
5
,
1
(
2020
).
7.
J.-G.
Lee
,
Deep Learning in Medical Imaging: General Overview
,
Korean Journal of Radiology
18
,
4
,
570
(
2017
).
8.
S.
Li
,
Multi-stage malaria parasite recognition by deep learning
,
GigaScience
10
,
6
(
2021
).
9.
S.
Li
,
Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL
),
Bioinformatics
36
,
16
,
4498
4505
(
2020
).
10.
S.
Li
,
Transfer Learning for Toxoplasma gondii Recognition
,
mSystems
5
,
1
(
2020
).
12.
M.
Poostchi
,
Image analysis and machine learning for detecting malaria
,
Translational Research
194
,
36
55
(
2018
).
13.
L. M.
Prevedello
,
Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions
,
Radiology: Artificial Intelligence
1
,
1
,
180031
(
2019
).
14.
M.
Umer
,
A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images
,
IEEE Access
8
,
93782
93792
(
2020
).
15.
S.
Wang
,
Artificial Intelligence in Lung Cancer Pathology Image Analysis
,
Cancers
11
,
11
,
1673
(
2019
).
16.
F.
Yang
,
Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears
,
IEEE Journal of Biomedical and Health Informatics
24
,
5
,
1427
1438
(
2020
).
17.
F.
Yang
, Smartphone-Supported Malaria Diagnosis Based on Deep Learning,
Machine Learning in Medical Imaging
.
Cham: Springer International Publishing
,
73
80
(
2019
).
18.
M.
Zare
,
A machine learning-based system for detecting leishmaniasis in microscopic images
,
BMC Infectious Diseases
22
,
1
(
2022
).
19.
C.
Zhang
,
Deep learning for microscopic examination of protozoan parasites
,
Computational and Structural Biotechnology Journal
20
,
1036
1043
(
2022
).
20.
L.
Zhang
,
S.
Wang
and
B.
Liu
,
Deep learning for sentiment analysis: A survey
,
WIREs Data Mining and Knowledge Discovery
8
,
4
(
2018
).
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