Multi-Class Microscopic Image Analysis of Protozoan Parasites Using Convolutional Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F24%3A43908124" target="_blank" >RIV/60076658:12310/24:43908124 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60076658:12520/24:43908124

Výsledek na webu

<a href="https://doi.org/10.3897/jucs.112639" target="_blank" >https://doi.org/10.3897/jucs.112639</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3897/jucs.112639" target="_blank" >10.3897/jucs.112639</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Class Microscopic Image Analysis of Protozoan Parasites Using Convolutional Neural Network

Popis výsledku v původním jazyce

Protozoan parasites cause a wide range of devastating diseases in various kinds of organisms, including humans. It may be lethal if untreated promptly. To detect specific diseasecausing parasites, a wide range of immunological and molecular technologies are now widely available. However, all of this depends on the worker&apos;s expertise and are time-consuming, errorprone, and expensive. With the development of technology, compared to traditional biological techniques, convolutional neural networks have reached excellent achievements in image classification, cutting costs while attaining an overall higher accuracy and eliminating human error. Many models include numerous convolutional layers and offer an accuracy between 90 and 95 percent. In this study, 4740 microscopic images of protozoan parasites from six classes with a balanced dataset and an 80-20% split were classified using three convolutional layers with stochastic gradient descent as an optimizer. A 5-fold cross-validation approach is used to evaluate the proposed method. We also examine and evaluate with deep learning models namely VGG16, ResNet50, and InceptionV3. The performance evaluation of the proposed model shows an accuracy of 94% with a precision range (of 0.83-0.99) and a recall range (of 0.76-1.00), respectively. The retrained model was able to recognize and classify all 6 different parasites. Except for class Leishmania, where 24% of images are incorrectly classified as Plasmodium and Trichomonas, the model demonstrates that most cases are correctly identified.

Název v anglickém jazyce

Multi-Class Microscopic Image Analysis of Protozoan Parasites Using Convolutional Neural Network

Popis výsledku anglicky

Protozoan parasites cause a wide range of devastating diseases in various kinds of organisms, including humans. It may be lethal if untreated promptly. To detect specific diseasecausing parasites, a wide range of immunological and molecular technologies are now widely available. However, all of this depends on the worker&apos;s expertise and are time-consuming, errorprone, and expensive. With the development of technology, compared to traditional biological techniques, convolutional neural networks have reached excellent achievements in image classification, cutting costs while attaining an overall higher accuracy and eliminating human error. Many models include numerous convolutional layers and offer an accuracy between 90 and 95 percent. In this study, 4740 microscopic images of protozoan parasites from six classes with a balanced dataset and an 80-20% split were classified using three convolutional layers with stochastic gradient descent as an optimizer. A 5-fold cross-validation approach is used to evaluate the proposed method. We also examine and evaluate with deep learning models namely VGG16, ResNet50, and InceptionV3. The performance evaluation of the proposed model shows an accuracy of 94% with a precision range (of 0.83-0.99) and a recall range (of 0.76-1.00), respectively. The retrained model was able to recognize and classify all 6 different parasites. Except for class Leishmania, where 24% of images are incorrectly classified as Plasmodium and Trichomonas, the model demonstrates that most cases are correctly identified.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Universal Computer Science

ISSN

0948-695X

e-ISSN

0948-6968

Svazek periodika

30

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

13

Strana od-do

420-432

Kód UT WoS článku

001237071800002

EID výsledku v databázi Scopus

2-s2.0-85193009261