Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays

Paúl Idrovo-Berrezueta; Denys Dutan-Sanchez; Vladimir Robles-Bykbaev

doi:10.1007/978-3-031-33258-6_24

Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays

Paúl Idrovo-Berrezueta
, Denys Dutan-Sanchez
, Vladimir Robles-Bykbaev

Grupo de Investigación de Inteligencia Artificial y Tecnologías de Asistencia (GI-IATA)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

The WHO (World Health Organization) has updated a publication where they discuss the topic of Covid-19 vaccination for children, and in this document, they mention the vulnerability that this illness has caused among children under the age of 5, exposing them to a higher risk of other diseases such as pneumonia. For this reason, this research is focused on the early detection of pneumonia using children’s chest X-rays and the implementation of artificial intelligence. CNN (Convolutional Neural Network) is the best tool to use as an image processor for the chest X-rays, hence a variety of deep learning techniques were used such as VGG16, VGG16-W VGG19, VGG19-W HT, ResNet50, ResNet50-W, MobileNet, and MobileNet-W. To enhance the accuracy of these deep learning techniques, transfer learning, and hyperparameters were applied to the training process. As a result of this research, we’ve obtained an accuracy of 0.9684 and a loss of 0.0793, and hope that with this research we can help the medical areas in the early detection of pneumonia and save doctors time.

Idioma original	Inglés
Título de la publicación alojada	Information Technology and Systems - ICITS 2023
Editores	Álvaro Rocha, Carlos Ferrás, Waldo Ibarra
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	263-272
Número de páginas	10
ISBN (versión impresa)	9783031332579
DOI	https://doi.org/10.1007/978-3-031-33258-6_24
Estado	Publicada - 2023
Evento	International Conference on Information Technology and Systems, ICITS 2023 - Cusco, Perú Duración: 24 abr. 2023 → 26 abr. 2023

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	691 LNNS

Conferencia

Conferencia	International Conference on Information Technology and Systems, ICITS 2023
País/Territorio	Perú
Ciudad	Cusco
Período	24/04/23 → 26/04/23

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Areas de Conocimiento del CACES

316A Desarrollo y análisis de software y aplicaciones
419A Tecnología de diagnóstico y tratamiento médico
8116A Sistemas de Información

Acceder al documento

10.1007/978-3-031-33258-6_24

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Citar esto

Idrovo-Berrezueta, P., Dutan-Sanchez, D., & Robles-Bykbaev, V. (2023). Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays. En Á. Rocha, C. Ferrás, & W. Ibarra (Eds.), Information Technology and Systems - ICITS 2023 (pp. 263-272). (Lecture Notes in Networks and Systems; Vol. 691 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-33258-6_24

Idrovo-Berrezueta, Paúl ; Dutan-Sanchez, Denys ; Robles-Bykbaev, Vladimir. / Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays. Information Technology and Systems - ICITS 2023. editor / Álvaro Rocha ; Carlos Ferrás ; Waldo Ibarra. Springer Science and Business Media Deutschland GmbH, 2023. pp. 263-272 (Lecture Notes in Networks and Systems).

@inproceedings{8b9450c75f2f423d80e1c8353fee3cfa,

title = "Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays",

abstract = "The WHO (World Health Organization) has updated a publication where they discuss the topic of Covid-19 vaccination for children, and in this document, they mention the vulnerability that this illness has caused among children under the age of 5, exposing them to a higher risk of other diseases such as pneumonia. For this reason, this research is focused on the early detection of pneumonia using children{\textquoteright}s chest X-rays and the implementation of artificial intelligence. CNN (Convolutional Neural Network) is the best tool to use as an image processor for the chest X-rays, hence a variety of deep learning techniques were used such as VGG16, VGG16-W VGG19, VGG19-W HT, ResNet50, ResNet50-W, MobileNet, and MobileNet-W. To enhance the accuracy of these deep learning techniques, transfer learning, and hyperparameters were applied to the training process. As a result of this research, we{\textquoteright}ve obtained an accuracy of 0.9684 and a loss of 0.0793, and hope that with this research we can help the medical areas in the early detection of pneumonia and save doctors time.",

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Idrovo-Berrezueta, P, Dutan-Sanchez, D & Robles-Bykbaev, V 2023, Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays. En Á Rocha, C Ferrás & W Ibarra (eds.), Information Technology and Systems - ICITS 2023. Lecture Notes in Networks and Systems, vol. 691 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 263-272, International Conference on Information Technology and Systems, ICITS 2023, Cusco, Perú, 24/04/23. https://doi.org/10.1007/978-3-031-33258-6_24

Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays. / Idrovo-Berrezueta, Paúl; Dutan-Sanchez, Denys; Robles-Bykbaev, Vladimir.
Information Technology and Systems - ICITS 2023. ed. / Álvaro Rocha; Carlos Ferrás; Waldo Ibarra. Springer Science and Business Media Deutschland GmbH, 2023. p. 263-272 (Lecture Notes in Networks and Systems; Vol. 691 LNNS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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AU - Dutan-Sanchez, Denys

AU - Robles-Bykbaev, Vladimir

PY - 2023

Y1 - 2023

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AB - The WHO (World Health Organization) has updated a publication where they discuss the topic of Covid-19 vaccination for children, and in this document, they mention the vulnerability that this illness has caused among children under the age of 5, exposing them to a higher risk of other diseases such as pneumonia. For this reason, this research is focused on the early detection of pneumonia using children’s chest X-rays and the implementation of artificial intelligence. CNN (Convolutional Neural Network) is the best tool to use as an image processor for the chest X-rays, hence a variety of deep learning techniques were used such as VGG16, VGG16-W VGG19, VGG19-W HT, ResNet50, ResNet50-W, MobileNet, and MobileNet-W. To enhance the accuracy of these deep learning techniques, transfer learning, and hyperparameters were applied to the training process. As a result of this research, we’ve obtained an accuracy of 0.9684 and a loss of 0.0793, and hope that with this research we can help the medical areas in the early detection of pneumonia and save doctors time.

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Idrovo-Berrezueta P, Dutan-Sanchez D, Robles-Bykbaev V. Comparison of Transfer Learning vs. Hyperparameter Tuning to Improve Neural Networks Precision in the Early Detection of Pneumonia in Chest X-Rays. En Rocha Á, Ferrás C, Ibarra W, editores, Information Technology and Systems - ICITS 2023. Springer Science and Business Media Deutschland GmbH. 2023. p. 263-272. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-33258-6_24