Deep-Learning based Identification of Frames Containing Foetal Gender Region during Early Second Trimester Ultrasound Scanning

Sex-selective abortions, based on ultrasound foetal scanning of pregnant women, is a rampant problem in developing countries like India. Although prenatal gender screening has been outlawed, poor enforcement of it has abetted a steady rise in their number. Currently, the B-scans are displayed on the screen in real-time and the operator can make out the gender from the displayed image. Therefore, enforcement of the secrecy has not been effective and has only led to limitations on the usage of the technology. Hence, it may be useful to develop a method that can identify frames containing gender-indicative features in real-time and block it out automatically from the display, thus, preventing unauthorized viewing. In this work, deep learning-based techniques have been explored to detect images containing the gender-defining features among the entire set of images in cine-loop, with a conforming accuracy averaging above 80%.