Extracting critical data from medical images based on machine learning

Sixuan Wang; Jingyi Chen; Shan Gong

doi:10.1117/12.2659592

22 November 2022 Extracting critical data from medical images based on machine learning

Sixuan Wang, Jingyi Chen, Shan Gong

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 1247520 (2022) https://doi.org/10.1117/12.2659592
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

Machine learning can be applied to cancer diagnosis, neurosurgery, radiotherapy, and so on. Its high accuracy and efficiency can improve the hospitals’ overall efficiency and save diagnostic time. This paper aims to provide an algorithm for extracting critical information from x-ray medical images. The extraction process was divided into two parts: 1) the pixel coordinates of cervical vertebra nodes via automatic localization and 2) patient-related data using OCR (optical character recognition). The extracted pixel coordinates and the text information in the image were then validated. Compared with manual processing of medical images, the proposed algorithm in this study provides higher efficiency, which can better serve doctors to evaluate patients’ medical images for further diagnoses. In the future, more and more medical images can be analyzed in a more intelligent and efficient way.

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Sixuan Wang, Jingyi Chen, and Shan Gong "Extracting critical data from medical images based on machine learning", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 1247520 (22 November 2022); https://doi.org/10.1117/12.2659592

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KEYWORDS

Medical imaging

Machine learning

Optical character recognition

Diagnostics

Image storage

Algorithm development

Detection and tracking algorithms

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