Colorectal cancer classification based on histology images: comparison between DNN and CNN

Jue Han; Deshang Kong

doi:10.1117/12.2672689

1 March 2023 Colorectal cancer classification based on histology images: comparison between DNN and CNN

Jue Han, Deshang Kong

Proceedings Volume 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022); 125960Y (2023) https://doi.org/10.1117/12.2672689
Event: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 2022, Changsha, China

Abstract

According to statistics from the World Health Organization, Colorectal Cancer (CRC) is the third most commonly diagnosed cancer in the world. The detection of CRC in an early stage is crucial for on-time and proper treatment, which may significantly increase the patient's survival rate. Although computers are not qualified to replace human experts at the moment, having a referential result from CRC auto-detection and saving the time of manual diagnosis is still very meaningful. This paper compares the performances of two different neural networks classifying CRC based on a set of histology images. The labeled dataset is publicly available on the Tensorflow website, and the two neural networks are tested on the same dataset separately. The first type of neural network in this study is Convolutional Neural Network (CNN), and the second type is a Deep Neural Network (DNN). As the dataset splits into training, testing, and validation sets, the loss, accuracy, and training time are recorded by the end of each epoch. The study result shows that the CNN method is better than the DNN method in terms of CRC image classification. It takes a long time but has better performance.

Citation Download Citation

Jue Han and Deshang Kong "Colorectal cancer classification based on histology images: comparison between DNN and CNN", Proc. SPIE 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 125960Y (1 March 2023); https://doi.org/10.1117/12.2672689

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KEYWORDS

Education and training

Error control coding

Data modeling

Colorectal cancer

Image classification

RGB color model

Neurons

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