Histopathological cancer detection: a comparison study of different convolutional neural networks

Zhuoyang Lyu

doi:10.1117/12.2639368

17 May 2022 Histopathological cancer detection: a comparison study of different convolutional neural networks

Zhuoyang Lyu

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122595Y (2022) https://doi.org/10.1117/12.2639368
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

The histopathological method is important for cancer diagnosis. The standard histopathological test today is extremely time-consuming, expensive, and labor-intensitive. However, as image digitalization technology became ubiquitous today, automatic analysis using computer-aided diagnosis tools provides a possible alternative. Using deep learning, especially for convolutional neural networks, it is possible to obtain results that have comparable performance to pathologists with a higher diagnosis speed. In this paper, seven different CNN architectures were trained on the Histopathological Cancer Detection dataset. The dataset contains 20000 images extracted from histopathological scans of lymph node sections. The results showed that there is a correlation between the performance and the architecture design’s appropriateness on a specific dataset. DenseNet achieved the best result on this dataset with an accuracy of 0.9182 without data augmentation, and 0.9268 with data augmentation. One possible reason for the superior performance of DenseNet is that DenseNet mimics the diagnosis process of pathologists by integrating image features with multiple different scales. The experiment results suggest that DenseNet can serve as an effective tool to speed up the diagnosis and ease the workload of pathologists.

Citation Download Citation

Zhuoyang Lyu "Histopathological cancer detection: a comparison study of different convolutional neural networks", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122595Y (17 May 2022); https://doi.org/10.1117/12.2639368

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KEYWORDS

Cancer

Neural networks

Convolution

Convolutional neural networks

RGB color model

Tissues

Data modeling

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