Convolutional Neural Networks for Early Computer Diagnosis of Child Dysplasia

Abstract

The problem in ultrasound diagnostics hip dysplasiais the lack of experience of the doctor in case of incorrect orientation of the hip joint andultrasound head. The aim of this study was to evaluate the ability of the convolutional neural network (CNN) to classifyand recognize ultrasound imagingof thehip joint obtained at the correct and incorrect position of the ultrasound sensor head in the computer diagnosisofpediatricdysplasia. CNN's suchas GoogleNet, SqueezeNet, and AlexNet were selected for the study. The most optimal for the task is the useof CNN GoogleNet showed. In this CNN usedtransfer learning. At the same time, fine-tuning of the network and additional training on the databaseof 97 standards of ultrasonic images of the hip jointwere applied. Image type RGB 32 bit, 210 × 300 pixels are used. Fine-tuning has been performedthe lower layers of the structure CNN, in which 5 classesare allocated, respectively 4 classes of hip dysplasia types according to the Graf, and the Type ERROR ultrasound image, where position of the ultrasoundsensor head and of the hip joint in ultrasound diagnostics are incorrect orientation.It was found that the authenticity of training and testing is the highestfor the GoogleNet network:when classified in the training group accuracy is up to 100%, when classified in the test group accuracy–84.5%