Comparative Study of Convolutional Neural Network Architecture in Lymphoma Detection

Michaella Yosephine; Rafita Erli Adhawiyah; Yasmin Salsabila Kurniawan; Isa Anshori; Ramadhita Umitaibatin; Vegi Faturrahman; Rey Ezra Langelo; Widyawardana Adiprawita; Hermin Aminah Usman; Okky Husain

doi:10.2991/978-94-6463-062-6_19

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Comparative Study of Convolutional Neural Network Architecture in Lymphoma Detection

Authors

Michaella Yosephine¹^{, *}, Rafita Erli Adhawiyah¹, Yasmin Salsabila Kurniawan¹, Isa Anshori¹, Ramadhita Umitaibatin¹, Vegi Faturrahman¹, Rey Ezra Langelo¹, Widyawardana Adiprawita¹, Hermin Aminah Usman², Okky Husain²

¹Bandung Institute of Technology, Bandung, Indonesia

²Padjadjaran University, Bandung, Indonesia

^*Corresponding author. Email: michaella.yosephine@gmail.com

Corresponding Author

Michaella Yosephine

Available Online 22 December 2022.

DOI: 10.2991/978-94-6463-062-6_19 How to use a DOI?
Keywords: Convolutional neural network; Lymphoma; MobileNet; ResNet50; VGG16
Abstract: In this study, we propose an automatic classification of three common types of lymphoma: (1) lymphoma, (2) benign lesion, and (3) carcinoma using lymphoma cell images magnified by 100x and by 400x. A comparative study was performed to find the best architecture to classify lymphoma cell images using the Keras library in Tensorflow. The architectures used in this study are ResNet50, MobileNetV1, and VGG16. Based on the accuracy of lymphoma classification for each architecture, the MobileNet model had the highest accuracy in all three classes at both 100x and 400x magnification levels, which suggests that MobileNet is the best model for lymphoma cell classification. This study can be later used as the base argument in modifying the MobileNet architecture further to get more accurate results in future similar studies.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 4th International Conference on Life Sciences and Biotechnology (ICOLIB 2021)
Series: Advances in Biological Sciences Research
Publication Date: 22 December 2022
ISBN: 10.2991/978-94-6463-062-6_19
ISSN: 2468-5747
DOI: 10.2991/978-94-6463-062-6_19 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Michaella Yosephine
AU  - Rafita Erli Adhawiyah
AU  - Yasmin Salsabila Kurniawan
AU  - Isa Anshori
AU  - Ramadhita Umitaibatin
AU  - Vegi Faturrahman
AU  - Rey Ezra Langelo
AU  - Widyawardana Adiprawita
AU  - Hermin Aminah Usman
AU  - Okky Husain
PY  - 2022
DA  - 2022/12/22
TI  - Comparative Study of Convolutional Neural Network Architecture in Lymphoma Detection
BT  - Proceedings of the 4th International Conference on Life Sciences and Biotechnology (ICOLIB 2021)
PB  - Atlantis Press
SP  - 193
EP  - 202
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-062-6_19
DO  - 10.2991/978-94-6463-062-6_19
ID  - Yosephine2022
ER  -

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