Development of a Genetic Method for X-ray Images Analysis based on a Neural Network Model

Abstract

Background: Modern medicine depends on technical advances in the field of medical instrumentation and the development of medical software. One of the most important tasks for doctors is determination of the exact boundaries of tumors and other abnormal formations in the tissues of the human body. Objective: The paper considers the problems and methods of machine classification and recognition of radiographic images, as well as the improvement of artificial neural networks used to increase the quality and accuracy of detection of abnormal structures on chest radiographs. Methods: A modified genetic method for the optimization of parameters of the model on the basis of a convolutional neural network was developed to solve the problem of recognition of diagnostically significant signs of pneumonia on an X-ray of the lungs. The fundamental difference between the proposed genetic method and existing analogs is in the use of a special mutation operator in the form of an additive convolution of two mutation operators, which reduces neural network training time and also identifies "oneighborhood of solutions" that is most suitable for investigation. Results: A comparative evaluation of the effectiveness of the proposed method and known methods was given. It showed an improvement in accuracy of solving the problem of finding signs of pathology on an X-ray of the lungs. Conclusion: Practical use of the developed method will reduce complexity, increase reliability of search, accelerate the process of diagnosis of diseases and reduce a part of errors and repeated inspections of patients.