Proceedings of the 4th International Conference on Life Sciences and Biotechnology (ICOLIB 2021)

Classification of Lymphoma, Benign Lesions, and Carcinoma Using Convolutional Neural Network

Authors
Hanina Nuralifa Zahra1, *, Isa Anshori1, Hasna Nadila1, Hofifa Mulya Utami1, Joshua Adi Chandra1, Muhammad Rashid Kurniawan1, Yunianti Khotimah1, Widyawardana Adiprawita1, Hermin Aminah Usman2, Okky Husain1
1Biomedical Engineering, Bandung Institute of Technology, Bandung, Indonesia
2Pathological Anatomy, Padjadjaran University, Bandung, Indonesia
*Corresponding author. Email: haninanz@gmail.com
Corresponding Author
Hanina Nuralifa Zahra
Available Online 22 December 2022.
DOI
10.2991/978-94-6463-062-6_18How to use a DOI?
Keywords
CNN; MobileNet; Inception; VGG16
Abstract

Lymphoma, carcinoma, and benign lesions are common diseases that have to go through several stages to be detected due to their structural similarity. A common way to distinguish these diseases is by analysing them manually based on the cell images at a certain magnification. However, this method still has many shortcomings in terms of accuracy as it is vulnerable to possible human error and requires quite a lot of time. Thus, an alternative faster and more accurate method of detection should be developed to increase patients’ chances of survival. One solution to overcome this problem is by using a deep learning model which mimics the behaviour of nerve cells in the human brain and has proven to be able to classify certain diseases in many studies. As there are many existing deep learning designs, this paper aims to explore the methods of detecting these diseases and find the best performance (highest accuracy) among them. With the microscope images data (magnifications of 100 and 400 times) provided by the medical faculty of the University of Padjadjaran (UNPAD), we investigated the classification result using different kinds of deep learning designs which were our designed CNNs and transfer learning using the Inception-V3, VGG16, and MobileNet. It was found that the best model used to classify images with a magnification of 100x is MobileNet (accuracy of 53% for benign lesions and 47% for lymphoma) and designed CNN (accuracy of 59% for benign lesions and 41% for lymphoma). While for image classification with a magnification of 400x, Inception-V3 showed the best result (accuracy of 80% for carcinoma and 50% for lymphoma).

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_18
ISSN
2468-5747
DOI
10.2991/978-94-6463-062-6_18How to use a DOI?
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.

Cite this article

TY  - CONF
AU  - Hanina Nuralifa Zahra
AU  - Isa Anshori
AU  - Hasna Nadila
AU  - Hofifa Mulya Utami
AU  - Joshua Adi Chandra
AU  - Muhammad Rashid Kurniawan
AU  - Yunianti Khotimah
AU  - Widyawardana Adiprawita
AU  - Hermin Aminah Usman
AU  - Okky Husain
PY  - 2022
DA  - 2022/12/22
TI  - Classification of Lymphoma, Benign Lesions, and Carcinoma Using Convolutional Neural Network
BT  - Proceedings of the 4th International Conference on Life Sciences and Biotechnology (ICOLIB 2021)
PB  - Atlantis Press
SP  - 175
EP  - 192
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-062-6_18
DO  - 10.2991/978-94-6463-062-6_18
ID  - Zahra2022
ER  -