Diagnosis of Lung Disease Based on Medical Images Using Artificial Neural Networks

The rapid development of technological advances, due to which computer algorithms for image analysis compete with professionals in terms of accuracy, but remain unchanged in speed and volume of cases. In recent years, advances in machine learning and the implementation of convolutional neural networks have improved the ability to classify and identify objects. There is strong evidence that such models can match or outperform experts in complex tasks such as imaging and word processing, recognition and classification, decision-making based on abstract representations, and even clinical decision-making. The purpose of this work is to study the existing methods of developing the architecture of the convolutional neural network and design our own model with subsequent implementation for the diagnosis of digital medical (X-ray) images for the presence of lung disease. As a result of the work, the methods of designing the architecture of convolutional neural networks were analyzed and researched, the main advantages and disadvantages of network optimization means were determined, and the software in which the trained model was implemented was developed.