Breast Cancer Classification Prediction Based on Ultrasonic Image Feature Recognition

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Oncology

Background:

• Managing complex breast cancer data and selecting predictive models requires ongoing research.
• Accurate breast cancer classification is crucial for effective treatment planning.

Purpose of the Study:

• To develop an intelligent system for real-time breast cancer diagnosis using ultrasound.
• To construct a breast cancer classification model based on ultrasound image features.
• To integrate machine vision and intelligent motion algorithms for automated diagnosis.

Main Methods:

• Ultrasound image feature extraction and classification model construction.
• Machine vision and intelligent motion algorithms for probe guidance and system structure.
• Coordinate transformation and image recognition for enhanced diagnostic processes.
• Machine learning algorithms for data processing and intelligent system development.

Main Results:

• An intelligent system for breast cancer classification and prediction was developed.
• The system integrates ultrasound imaging, machine vision, and machine learning.
• Experimental verification demonstrated the system's effectiveness in breast cancer classification.

Conclusions:

• The developed breast cancer classification prediction system based on ultrasonic image feature recognition shows promising results.
• The intelligent system offers a potential advancement in real-time, automated breast cancer diagnosis.
• Further clinical verification is suggested for widespread adoption.