Deep Learning for Lymphoma Detection on Microscopic Images

DOI

10.2991/978-94-6463-062-6_20How to use a DOI?

Keywords

Lymphoma; InceptionResNetV2; Deep learning

Abstract

Early lymphoma diagnosis is essential to improve the patients’ survival rate and avoid irreversible damage. Immunohistochemistry-based lymphoma diagnostics is an expensive and time-consuming process, especially in developing countries with limited resources. Image-based lymphoma diagnostics might serve as an inexpensive, yet less accurate alternative to immunohistochemistry-based methods. One challenge in image-based methods is that carcinoma can occur in the same organ as lymphoma, thus making it hard to differentiate the two types of cancer. To assist lymphoma diagnostics, this study proposes a deep learning-based method to classify nasopharyngeal microscopic biopsy images into one of three classes: lymphoma, carcinoma, and benign lesion. The method works by splitting the images into patches, classifying each patch using a deep learning model, and taking the average confidence score of each patch. We compared three deep learning-based feature extractor architectures and studied the effects of three image color preprocessing techniques on classification performance. We reached 88.7% sensitivity and 91.3% specificity in differentiating lymphoma on 400x magnification CLAHE-enhanced microscopic images using the InceptionResNetV2 model. We also reached 87.0% three-class classification accuracy using the same model.

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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TY  - CONF
AU  - Ammar Ammar
AU  - Irfan Tito Kurniawan
AU  - Resfyanti Nur Azizah
AU  - Hafizh Rahmatdianto Yusuf
AU  - Antonius Eko Nugroho
AU  - Ghani Faliq Mufiddin
AU  - Isa Anshori
AU  - Widyawardana Adiprawita
AU  - Hermin Aminah Usman
AU  - Okky Husain
PY  - 2022
DA  - 2022/12/22
TI  - Deep Learning for Lymphoma Detection on Microscopic Images
BT  - Proceedings of the 4th International Conference on Life Sciences and Biotechnology (ICOLIB 2021)
PB  - Atlantis Press
SP  - 203
EP  - 215
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-062-6_20
DO  - 10.2991/978-94-6463-062-6_20
ID  - Ammar2022
ER  -