A Poster on Intestinal Parasite Detection in Stool Sample Using AlexNet and GoogleNet Architectures

Nakasi, Rose; Aliija, Ezra Rwakazooba; Nakatumba, Joyce

doi:10.1145/3460112.3472309

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Por otro lado, los enfoques basados en la utilización de redes profundas pre entrenadas se presentan en (Huq y Pervin, 2020;Nakasi et. al, 2021;Var y Boray, 2018;Bharadwaj y Sujitha, 2019;Chakradeo et.…”

Section: Trabajos Relacionadosunclassified

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Clasificación de parásitos en imágenes de copro con transferencia de aprendizaje y aumento de datos

Velázquez

Murguía

Quintana

et al. 2021

recibe

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Los seres humanos pueden albergar parásitos, por lo tanto, es fundamental una detección temprana para prevenir enfermedades. Los parásitos pueden observarse a través de imágenes microscópicas, con lo cual, la visión por computadora se muestra como un enfoque que pueda ayudar a la detección y clasificación de parásitos en imágenes digitales. Los modelos de aprendizaje profundo han mostrado un desempeño formidable en la clasificación de imágenes, debido a esto, en este artículo se presentan varios clasificadores profundos multiclase para reconocer 8 clases: 7 tipos de parásitos y la clase no parásitos. Los clasificadores diseñados utilizan transferencia de aprendizaje basada en la arquitectura AlexNet modificada. Al contar con una cantidad de muestras escasa de imágenes de parásitos, se realizó también un aumento de datos, tanto con el método tradicional como mediante la generación de imágenes con una red generativa adversaria (GAN) diseñada para ese propósito. El clasificador con mejor desempeño presentó una exactitud del 99.94%, 98.97% y 98.18% en el conjunto de entrenamiento, validación y de prueba, respectivamente.

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Section: Trabajos Relacionadosunclassified

“…El desempeño fue de 95.96%. En (Nakasi et. al, 2021) se clasifican parásitos intestinales en muestras de heces.…”

Section: Trabajos Relacionadosunclassified

Clasificación de parásitos en imágenes de copro con transferencia de aprendizaje y aumento de datos

Velázquez

Murguía

Quintana

et al. 2021

recibe

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Deep Transfer Learning in Parasites Imaging: A Systematic Review

Kumar,

Arif

2024

Lecture Notes in Networks and Systems

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Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions

Kumar

Arif

Alotaibi

et al. 2022

Arch Computat Methods Eng

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In the developing world, parasites are responsible for causing several serious health problems, with relatively high infections in human beings. The traditional manual light microscopy process of parasite recognition remains the golden standard approach for the diagnosis of parasitic species, but this approach is time-consuming, highly tedious, and also difficult to maintain consistency but essential in parasitological classification for carrying out several experimental observations. Therefore, it is meaningful to apply deep learning to address these challenges. Convolution Neural Network and digital slide scanning show promising results that can revolutionize the clinical parasitology laboratory by automating the process of classification and detection of parasites. Image analysis using deep learning methods have the potential to achieve high efficiency and accuracy. For this review, we have conducted a thorough investigation in the field of image detection and classification of various parasites based on deep learning. Online databases and digital libraries such as ACM, IEEE, ScienceDirect, Springer, and Wiley Online Library were searched to identify sufficient related paper collections. After screening of 200 research papers, 70 of them met our filtering criteria, which became a part of this study. This paper presents a comprehensive review of existing parasite classification and detection methods and models in chronological order, from traditional machine learning based techniques to deep learning based techniques. In this review, we also demonstrate the summary of machine learning and deep learning methods along with dataset details, evaluation metrics, methods limitations, and future scope over the one decade. The majority of the technical publications from 2012 to the present have been examined and summarized. In addition, we have discussed the future directions and challenges of parasites classification and detection to help researchers in understanding the existing research gaps. Further, this review provides support to researchers who require an effective and comprehensive understanding of deep learning development techniques, research, and future trends in the field of parasites detection and classification.

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A Poster on Intestinal Parasite Detection in Stool Sample Using AlexNet and GoogleNet Architectures

Cited by 5 publications

References 12 publications

Clasificación de parásitos en imágenes de copro con transferencia de aprendizaje y aumento de datos

Clasificación de parásitos en imágenes de copro con transferencia de aprendizaje y aumento de datos

Deep Transfer Learning in Parasites Imaging: A Systematic Review

Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions

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