Classification of Ultrasound Images with Convolutional Neural Networks in High-Performance Using IoT

Abstract

This paper contains the specification of functional and nonfunctional requirements of a CNN (Convolutional Neural Network) model to identify masses in liver ultrasounds. A neural network model based on CNN was implemented, which performs the analysis of ultrasound images of the liver, identifying the presence of masses or not in them. CNN can be executed either on a machine with a CPU (Central Processing Unit) or in a high processing capacity environment that uses GPU (Graphics Processing Unit). The validation of the accuracy of the obtained results happens through the statistical technique of k-fold cross-validation using IoT. During the neural network training process, dense data necessitates a lot of computational resources. Because the number of network entries is precisely proportional to the number of pixels in the image, parallel processing for image processing is justified. It is important to remember that medical photos cannot be saved in a lossy format and must be identified with exceptional accuracy using IoT.