The work highlights neural network applications to medical images, namely X-ray images. An overview of neural networks used to analyze medical images was conducted. Such a neural network has been implemented and tested on third-party images.
In today’s world, the amount of available information grows exponentially every day. Most of this data is visual data. Correspondingly, the demand for the algorithm of image rent is growing. Traditionally, the first approaches to computer vision problems were classical algorithms without the use of machine learning. Such approaches are limited by many factors. First of all, the conditions imposed on the input images are applied – the shooting angle, lighting, position of objects on the scene, etc. Other classical algorithms cannot meet the needs of modern computer vision problems.
Image classification task is a very important problem of a computer vision area. The first approaches to image classification tasks belong to a classic straightforward algorithm. Despite the successful applications of such algorithms a lot of image classification tasks had not been solved until machine learning approaches were involved in a computer vision area. An early successful result of machine learning applications helps researchers with extracted features classification which was not available without machine learning models.
This paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed.