Lymphoma recognition based on CNN models

Feiyang Zhang; Shanglong Yang; Shuaiwei Guo; Xu Xia

doi:10.1117/12.2623096

1 December 2021 Lymphoma recognition based on CNN models

Feiyang Zhang, Shanglong Yang, Shuaiwei Guo, Xu Xia

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120792I (2021) https://doi.org/10.1117/12.2623096
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Being part of hematopoietic lymphoid tissue tumors, lymphoma has aroused wide concerns in the field of cancer research. Therefore, a lymphatic cancer diagnosis has become a popular question in medical image processing, with numerous works are proposed. Recent works focus on how to get the appropriate image for a better cancer diagnosis. However, the question of how to get a higher accuracy just by learning from the dataset of the images of lymphatic node sections has not been paid attention to. This paper aims to solve this problem by applying a convolutional neural network (CNN), which has nine convolution layers, four dropout layers, three maxpooling layers, two dense layers, and one flatten layer in total. Experiments on the PatchCamelyon dataset show that our method can handle the Lymphoma cancer diagnosis problem with an accuracy of 93.15%, much higher than that of the LeNet and AlexNet, which are 83.17% and 84.49%, respectively. With such high accuracy, our model has the potential of acting as a reliable assistant for doctors when diagnosing lymphoma cancers.

Citation Download Citation

Feiyang Zhang, Shanglong Yang, Shuaiwei Guo, and Xu Xia "Lymphoma recognition based on CNN models", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120792I (1 December 2021); https://doi.org/10.1117/12.2623096

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KEYWORDS

Convolution

Cancer

Lymphoma

Lymphatic system

Data modeling

Tumor growth modeling

Tumors

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