Abstract
Abdominal B-ultrasound images of intrauterine pregnancy tissue residues were analyzed to discuss their diagnostic value. With the rapid development of computer technology and medical imaging technology, doctors are also faced with more and more medical image diagnosis tasks, and computer-aided diagnosis systems are especially important in order to reduce the work pressure of doctors. In recent years, deep learning has made rapid development and achieved great breakthroughs in various fields. In medical-aided diagnostic systems, deep learning has greatly improved the diagnostic efficiency, but there are no mature research results for abdominal B-ultrasound image recognition of intrauterine pregnancy tissue residues. Therefore, the study of liver ultrasound image classification based on deep learning has important practical application value. In this paper, we propose to give a CNN model optimization method based on grid search. Compared with the conventional CNN model design, this method saves time and effort by eliminating the need to manually adjust parameters based on experience and has an accuracy of more than 92% in classifying abdominal B-ultrasound images of intrauterine pregnancy tissue residues. The diagnosis of intrauterine pregnancy tissue residues by abdominal B-ultrasound can effectively improve the diagnosis and provide important reference for patients to receive treatment, which has high diagnostic value.
Highlights
Residual intrauterine pregnancy tissue is a residual appendage or detrusor that remains in the uterine cavity after termination of intrauterine pregnancy, which can cause recurrent vaginal bleeding or uterine infection, and is an important cause of abortion or postpartum uterine bleeding and infection [1]
In order to verify the effectiveness of the model scaling method in this experiment, a comparison of the accuracy of the scaling method in this experiment with that of the scaling performed in a single dimension was first performed under the same Flops required for CNN. e three methods are increasing only the depth of CNN, i.e., the number of layers, increasing only the width of CNN model, and the method proposed in this experiment, compounding three dimensional parameters to expand the CNN model at the same time
Intrauterine pregnancy residue is a dangerous disease in obstetrics and gynecology, mainly caused by excessive curvature of the uterus. It is commonly seen in patients who have had a medication abortion at a long gestational age, in those who have undergone a poorly performed abortion, in those who have undergone an induction of labor in mid-pregnancy, resulting in adhesions between the fetal membranes and the placenta, and in those who have had abnormal changes in the morphology of the uterine cavity
Summary
Residual intrauterine pregnancy tissue is a residual appendage or detrusor that remains in the uterine cavity after termination of intrauterine pregnancy, which can cause recurrent vaginal bleeding or uterine infection, and is an important cause of abortion or postpartum uterine bleeding and infection [1]. E clinical characteristics of abdominal ultrasound images of intrauterine pregnancy residues after delivery in mid- to lateterm pregnancy were studied and the diagnostic effect of ultrasound images was investigated. Forty patients admitted to our hospital with suspected intrauterine pregnancy residues after delivery in mid- to late-term pregnancy underwent ultrasound examination, and those with abnormalities underwent surgical pathological examination. Ultrasound imaging has a good diagnostic effect on the Journal of Healthcare Engineering diagnosis of intrauterine pregnancy residues after delivery in mid- to late-term pregnancy and can accurately reflect the specific location, size, and relationship between the residues and the uterine wall while detecting intrauterine pregnancy residues. It is worthy to be widely promoted in clinical practice
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