A neuro-fuzzy system for automated detection and classification of human intestinal parasites

Nkamgang, Oscar Takam; Tchiotsop, Daniel; Tchinda, Beaudelaire Saha; Fotsin, Hilaire Bertrand

doi:10.1016/j.imu.2018.10.007

Cited by 21 publications

(13 citation statements)

References 17 publications

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“…Finally, for the recognition and classification of different types of intestinal parasites Takam et al (Nkamgang et al 2018 ) proposed a neuro-fuzzy system, which automatically classifies these parasites by microscopic images, which are provided as input. These images are preprocessed with different techniques for the detection of parasites and once having this, the information is provided to the neuro-fuzzy classifier to perform the classification.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

et al. 2022

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Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of the C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm’s performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also one of the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.

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Section: Related Workmentioning

confidence: 99%

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

et al. 2022

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“…No artigo [12] foi proposto um software que analisa automaticamente imagens microscópicas e consegue classificar parasitos intestinais. A abordagem é baseada na segmentac ¸ão e treinamento de um classificador, utilizando a combinac ¸ão de um sistema neuro-fuzzy e uma RNA.…”

Section: Trabalhos Relacionadosunclassified

Uma Abordagem de Classificação de Ovos de Parasitos Intestinais Humanos em Imagens Microscópicas usando Redes Neurais Convolucionais

Oliveira¹,

Moreira²,

Coelho³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Uma das maiores preocupações da área de saúde pública são causadas pelos parasitos intestinais humanos, que são encontrados em grande parte nos países tropicais. O diagnóstico dessas doenças parasitárias se dá por meio de sintomas fisiológicos e exame fecal. Frequentemente, poucos profissionais estão disponíveis e aptos a realizarem esse tipo de exame, que é considerado lento, difícil, propenso a erros e pode causar fadiga ocular no especialista. Portanto, o objetivo desse trabalho é utilizar redes neurais convolucionais para classificar ovos de parasitos intestinais, sendo um sistema de auxílio a tomada de decisão no diagnóstico de um exame de fezes. Foram realizados experimentos empíricos de modo a definir uma arquitetura da rede específica para cada problema. Os resultados obtidos demonstraram uma taxa de reconhecimento de 99.9%, para todas as métricas avaliadas. A aplicação desenvolvida será parte essencial de um futuro sistema totalmente automatizado.

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“…Finally, for the recognition and classification of different types of intestinal parasites Takam et al [31] proposed a neuro-fuzzy system, which automatically classifies these parasites by microscopic images, which are provided as input. These images are preprocessed with different techniques for the detection of parasites and once having this, the information is provided to the neuro-fuzzy classifier to perform the classification.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Dynamic Parameter Adaptation in the Bird Swarm Algorithm for Neural Network Optimization

Melín

Miramontes

Carvajal

et al. 2021

Preprint

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Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm's performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses patient information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.

show abstract

A neuro-fuzzy system for automated detection and classification of human intestinal parasites

Cited by 21 publications

References 17 publications

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

Uma Abordagem de Classificação de Ovos de Parasitos Intestinais Humanos em Imagens Microscópicas usando Redes Neurais Convolucionais

Fuzzy Dynamic Parameter Adaptation in the Bird Swarm Algorithm for Neural Network Optimization

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