AI Method for Classification of Diagnosis of Near-Infrared Breast Lesion Images

Authors

• Kaiquan Chen, Yeshiva University
• Fangyang Shen, CUNY New York City College of TechnologyFollow
• Honggang Wang, Yeshiva University
• Zhengchao Dong, Vagelos College of Physicians and Surgeons, Columbia University
• Jizhong Xiao, CUNY City CollegeFollow
• Ming Ma, Yeshiva University
• Afroza AktarFollow
• Christopher Chow, CUNY New York City College of TechnologyFollow
• Wenxiong Zhang, CUNY New York City College of Technology

Document Type

Article

Publication Date

4-7-2026

Abstract

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, have been developed to assist this process. However, these tools are primarily manual or semi-automated and are unable to provide rapid and high-precision recognition. To address these limitations, this study proposes a new AI-based method for the rapid, accurate, and fully automated detection and diagnosis of breast lesions. The proposed approach complements existing AI-based annotation and diagnostic methods by enabling automated detection and classification of breast lesion samples. The proposed system employs a deep learning–based classification framework to construct a professional-level AI diagnostic model. The system automatically generates diagnostic outputs based on the annotation criteria used by professional physicians, including positive/negative classification and accuracy metrics. Compared with conventional manual diagnostic methods, the proposed approach provides faster and more reliable diagnostic estimates for new patients. These results demonstrate the potential of the proposed AI-based method to advance automated breast lesion screening and diagnosis and to contribute to future research and clinical applications in this field.

Comments

This article was originally published in AI, available at https://doi.org/10.3390/ai7040133. 

This work is distributed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).