Article Details

Abstract

AI-based ultrasound image Object Detection and Recognition algorithms usually adopt Python programming, while Java remains the preferred language for project development. AI information systems for ultrasound image Object Detection and Recognition based on Java-Python joint programming are still rare. This paper attempts to develop an AI information system based on Java-Python joint programming to realize the detection and recognition of hepatic echinococcosis lesions in ultrasound images. The system front-end adopts UniApp to design the human-computer interaction interface. The back-end builds a Python ultrasound image lesion Object Detection and Recognition server and a Java application server. It implements communication between servers through Socket protocol, returns detection results to the Java application back-end for processing in JSON format, and then feeds them back to the mobile terminal front-end for display. It also generates corresponding diagnosis and treatment suggestions according to lesion categories. Tests show that this AI information system based on joint programming effectively realizes the detection and recognition of lesion targets. It not only has high detection and recognition accuracy and good real-time performance, but also provides auxiliary diagnosis and decision support for clinicians. Furthermore, it can be used for practical teaching of ultrasound image lesion detection and recognition, and provides a method reference for researchers with similar AI information system development needs.

Keywords

Java-Python joint programming; AI information system; hepatic echinococcosis; ultrasound image; Object Detection and Recognition

References

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Funding

SRTP Project of Xinjiang Medical University (X202310760154); Construction project of the "Medical Image Processing" virtual experimental platform for first-class undergraduate courses at the Xinjiang Uygur Autonomous Region level (virtual simulation experimental platform for artificial intelligence interpretation of liver hydatid cyst images)

About the authors

Teng Jun (2002-), male, Miao ethnic group, from Huaihua, Hunan, undergraduate student, research direction: computer applications; Corresponding author: Yan Chuanbo (1970-), male, Han ethnicity, from Anyue, Sichuan, professor, undergraduate, research direction: medical image processing, computer applications.