An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

Research on Automatic Diagnostic Technology of Soybean Leaf Diseases Based on Improved Transfer Learning

Soybean diseases and insect pests are important factors that affect the output and quality of the soybean,thus,it is necessary to do correct inspection and diagnosis on them.For this reason,based on improved transfer learning,a classification method of the soybean leaf diseases was proposed in this paper.In detail,this method first removed the complicated background in images and cut apart leaves from the entire image;second,the data-augmented method was applied to amplify the separated leaf disease image dataset to reduce overfitting;at last,the automatically fine-tuning convolutional neural network(AutoTun)was adopted to classify the soybean leaf diseases.The proposed method respectively reached 94.23%,93.51%and 94.91%of validation accuracy rates on VGG-16,ResNet-34 and DenseNet-121,and it was compared with the traditional fine-tuning method of transfer learning.The results indicated that the proposed method had superior to the traditional transfer learning method.

A novel feature extraction method for ship-radiated noise

To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.