@inproceedings{8feffaa8c6b84c0896ad3263802b3974,
title = "Automatic Classification and Diagnosis of Pathological Section Microimaging Based on Deep Learning",
abstract = "In this paper, an automatic classification and diagnosis method of pathological section micro imaging based on deep learning is proposed. In order to overcome the problems of slow and low accuracy in identifying diseased tissues during the microscopic examination and diagnosis of pathological sections, we realized automatic classification of pathological sections using the EfficientNet-B7 network, and adjusted the scanning step size in real time to locate diseased tissues. The results show that the detection method is very effective and can quickly and accurately identify the location of the diseased tissue.",
keywords = "automatic scanning, deep learning, medical image classification, pathological section",
author = "Miao Zhang and Xiaoyi Qiao and Haihan Zhu and Ziang Chen and Jiling Xiao and Yi Shen",
note = "Publisher Copyright: {\textcopyright} 2025 Technical Committee on Control Theory, Chinese Association of Automation.; 44th Chinese Control Conference, CCC 2025 ; Conference date: 28-07-2025 Through 30-07-2025",
year = "2025",
doi = "10.23919/CCC64809.2025.11179676",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8774--8779",
editor = "Jian Sun and Hongpeng Yin",
booktitle = "Proceedings of the 44th Chinese Control Conference, CCC 2025",
address = "美国",
}