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.
Cite this article
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 -