Semi-supervised deep learning for automatic classification of medical images

Huiliang Shang; Peizhe Wu

doi:10.1117/12.3032744

12 July 2024 Semi-supervised deep learning for automatic classification of medical images

Huiliang Shang, Peizhe Wu

Author Affiliations +

Proceedings Volume 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) ; 1318503 (2024) https://doi.org/10.1117/12.3032744
Event: International Conference on Communication, Information and Digital Technologies, 2024, Wuhan, China

Abstract

In recent years, the prevalence rate of laryngeal cancer is gradually rising, and the best treatment at present lies in early diagnosis and prevention. Early laryngeal cancer is difficult to be detected, and imaging examination is one of the important methods for diagnosis and prevention of laryngeal cancer. However, due to the particularity of medical images, the human eye is often difficult to pay attention to all characteristic information, and doctors lacking experience in endoscopy are prone to misjudgment. Using deep learning to build a computer-aided diagnosis system for laryngeal diseases is an effective solution. At present, semi-supervised learning has become a research hotspot in the field of medical image classification. The intent of this study is to construct and evaluate the performance of a technique based on self-training and consistent regularization for the classification of laryngeal disease images. Our dataset contains a total of 4000 laryngeal images, including 3000 normal images and 1000 laryngeal cancer images. We divide the sample set into two parts: learning samples and validation samples. The distribution ratio is 8:2. Thirty percent of the samples were labeled and seventy percent were unlabeled. Experimental data show that the classification accuracy of the model can reach 80.125%. The classifier is helpful to develop a computer-aided diagnosis system for laryngeal diseases and can effectively decrease the annotation cost.

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

Citation Download Citation

Huiliang Shang and Peizhe Wu "Semi-supervised deep learning for automatic classification of medical images", Proc. SPIE 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) , 1318503 (12 July 2024); https://doi.org/10.1117/12.3032744

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KEYWORDS

Data modeling

Image classification

Machine learning

Education and training

Medical imaging

Cancer

Deep learning

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