A deep learning enhanced graylevel co-occurrence matrix approach for cancer classification using cytological smears

Yifei Wang; Yu Zhao; Bo Yu

doi:10.1117/12.3029819

21 June 2024 A deep learning enhanced graylevel co-occurrence matrix approach for cancer classification using cytological smears

Yifei Wang, Yu Zhao, Bo Yu

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671L (2024) https://doi.org/10.1117/12.3029819
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Cytological smears play an important role in disease diagnosis, particularly for specific cancers such as thyroid and cervical cancer, where cytological smears are primarily cell smears. With the rapid development of deep learning technology, more and more people are using deep neural networks to classify and discriminate cancers. However, getting ethically certified and clear cytological smears is hard, so training is always based on small data sets. When facing small cell smears with different levels of brightness and clarity, which are collected from various devices and environments, models' performance is often limited. Given the above situation, we have designed a cytological smear classification model that is trained on an augmented data set and considered the gray-level co-occurrence matrix of the image, making the model perform better when faced with noisy images, and we call it the Glcm-BoT model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yifei Wang, Yu Zhao, and Bo Yu "A deep learning enhanced graylevel co-occurrence matrix approach for cancer classification using cytological smears", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671L (21 June 2024); https://doi.org/10.1117/12.3029819

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