A Machine Learning-Based System for Detecting Fetal Brain Abnormalities

Abstract

The invention discloses a method for detecting fetal brain abnormalities based on machine learning, specifically detecting whether there is abnormality in the main tissue structure of the fetal brain and which related brain diseases will be caused by the abnormality during the development process of the fetal brain. The present invention mainly obtains the data of standard sections of cranial brain in different gestational weeks, preprocesses the data, and trains a model to detect whether there is abnormality in the main organizational structure of the cranial brain. The deep convolutional network is used to extract features, the region generation network RPN generates candidate regions, the interest pooling layer collects the input feature maps and candidate regions, and uses the softmax classifier for classification and regression detection, and finally uses the detection results to analyze the main Whether there is an anomaly in the structure. If no abnormality is found, it is judged as normal. The present invention aims to use computer-aided diagnosis to help diagnose whether the cranial brain is abnormal without the need for doctors or humans to participate in the diagnosis too much.

Description

technical field [0001] The invention belongs to the technical field of computer-aided diagnosis, and more specifically relates to a system for detecting fetal brain abnormalities based on machine learning. Background technique [0002] During the development of the whole body of the fetus, the healthy development of the brain is of great significance. Brain hypoplasia will directly affect the intelligence of the fetus, and seriously cause cerebral palsy, mental retardation, mental retardation, and epilepsy. In view of this, it is necessary to conduct a detailed examination of the fetal brain by prenatal ultrasound. [0003] However, there are some non-negligible shortcomings in the existing ultrasound for fetal brain detection: first, because the fetal brain detection process is quite complicated, and there is a serious shortage of sonographers with rich clinical experience and good at prenatal detection of fetal brain abnormalities, so Greatly increased the work tasks of t...

AI Technical Summary

Problems solved by technology

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a system for detecting fetal brain abnormalities based on machine learning, the purpose of which is to deeply learn the normal and Abnormal fetal brain ultrasound image data solves the above-mentioned technical problems in the existing fetal brain detection based on ultrasound imaging. In the process of real-time ultrasound scanning of the fetus, the present invention combines the fetal gestational age and medical history and other data in real time. Intermittently monitor and analyze fetal brain ultrasound images to intelligently identify a series of standard sections of fetal brain development, automatically acquire and store the intelligently identified standard sections, and finally automatically measure and analyze fetal brain development parameters, and identify possible fetal craniocerebral abnormalities

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