LIVER ULTRASOUND IMAGING LESION DETECTION BASED ON YOLO

Abstract

The liver is a silent organ with no pain-sensing nerves. When the body's functions begin to appear abnormal, it may have entered the trilogy of liver diseases: "hepatitis, cirrhosis, and liver cancer." An abdominal ultrasound is a powerful tool for checking liver health. With the rapid development of ultrasound technology, ultrasound machines are gradually developing in the direction of miniaturization and cheapness, transforming from hospital-specific equipment to home medical equipment. However, ultrasound diagnosis requires professional knowledge, which has also become the threshold for the popularization of ultrasound diagnosis. This article uses artificial intelligence and machine learning technology to take the liver ultrasound images marked by professional doctors as a data set. After training of image object detection model YOLO, it can be used to detect tumors, hepatic hemangioma, radiofrequency cautery, abscess and metastatic, the accuracy can reach 98%.