The research is devoted to the construction of a convolutional neural network model for recognizing medical images on the example of X-ray images database of patients with an established diagnosis of brain tumor. The convolutional neural network model is proposed, the architecture of which includes two convolutional layers and one fully connected layer. The accuracy results of the proposed classifier and accuracy results of the pre-trained models VGG16, VGG19, Inception-V3, InceptionResNet-V2, ResNet50, ResNet152 and Xception are compared. The considered CNN models on the test dataset achieved the image recognition accuracy from 95.36% to 98.84%. The highest accuracy of the results in solving the problem of recognizing a brain tumor was achieved by the models VGG 16, VGG 19, Xception and the proposed model. However, the training time of the constructed models differs depending on the architecture of the neural network. At the same time, for the proposed CNN model, there 0.783% were no detected signs of the disease among the X-ray samples of patients with an established diagnosis of a brain tumor. The proposed neural network model can act as an additional tool of a doctor in the diagnosis of a brain tumor. The introduction of computer vision algorithms into the daily work of a doctor will make it possible to promptly carry out an additional examination of the patient, make a diagnosis and carry out treatment in a timely manner. The use of services based on artificial intelligence algorithms can reduce the total time spent on diagnostic studies, identify pathologies at an early stage of the disease and are more likely to expect that treatment will lead to positive results.
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