Determination and classification of fetal sex on ultrasound images with deep learning

Today, various prenatal diagnostic methods are used to determine the sex of the fetus. All of these medical methods require intervention by a specialist. The sensitivity of fetal ultrasonography (USG) scanning, which is the most commonly used diagnostic method, is variable and depends on the experience of the sonographer. In this study, an automatic, objective and reliable determination of fetal sex was aimed at using deep transfer learning techniques on USG images. For the study, a dataset containing 4400 fetal USG images, of which sexes were labeled by a gynecologist expert in the field, was created. In the first step, images were classified with finetuned convolutional neural networks. Following this classification, the fine-tuned DenseNet201 (ft-DenseNet201) network, which gave the most successful result with an accuracy of 0.9627, was used as the feature extractor network in the second step. Obtained features were classified by Logistic Regression (LR), Linear Support Vector Machine (LSVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest and AdaBoost algorithms. Among the 10 different classifiers used in the application, ft-DenseNet201 + LSVM (0.9782), ft-DenseNet201 + KNN (0.9727) and ft-DenseNet201 + LR (0.9718) algorithms gave very high accuracy values. This study can be evaluated as an automatic, objective, reliable and new medical method in determination of fetus sex; and can be used as an auxiliary system for specialists and patients by being integrated with USG devices.