DOI: 10.14489/vkit.2022.06.pp.040-050

Луценко В. С., Шухман А. Е.
СЕГМЕНТАЦИЯ МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ СВЕРТОЧНЫМИ НЕЙРОННЫМИ СЕТЯМИ
(с. 40-50)

Аннотация. Рассмотрены архитектуры сверточных нейронных сетей, их преимущества и недостатки. Созданы три модели нейронных сетей для сегментации медицинских изображений, проведено их обучение и тестирование. По результатам тестирования определена наиболее подходящая для сегментации медицинских изображений модель нейронной сети. Описан алгоритм, использующий предсказания всех созданных моделей нейронных сетей и продемонстрировавший более точный результат, чем каждая из рассмотренных моделей в отдельности.

Ключевые слова:  сверточные нейронные сети; нейронные сети; машинное обучение; глубокое обучение; сегментация; классификация.

Lutsenko V. S., Shukhman A. E.
SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS
(pp. 40-50)

Abstract. Our study briefly discusses the architectures of convolutional neural networks (CNN), their advantages and disadvantages. The features of the architecture of the convolutional neural network U-net are described. An analysis of the CNN U-net was carried out, based on the analysis, a rationale was given for choosing the CNN U-net as the main architecture for using and building subsequent created and analyzed models of cert neural networks to solve the problem of segmentation of medical images. The analysis of architectures of convolutional neural networks, which can be used as convolutional layers in CNN U-net, has been carried out. Based on the analysis, three architectures of convolutional neural networks were selected and described suitable for use as convolutional layers in CNN U-net. Using CNN U-net and three selected convolutional neural networks (“resnet34”, “inceptionv3” and “vgg16”), three neural network models for medical image segmentation were created. The training and testing of the created models of neural networks was carried out. Based on the results of training and testing, an analysis of the obtained indicators was carried out. Experiments were carried out with each of the three constructed models (segmentation of images from the validation set was performed and segmented images were presented). Based on the testing indicators and empirical data obtained from the results of the experiments, the most suitable neural network model created for solving the problem of medical image segmentation was determined. The algorithm for segmentation of medical images has been improved. An algorithm is described that uses the predictions of all created models of neural networks, which demonstrated a more accurate result than each of the considered models separately.

Keywords: Convolutional neural networks; Neural networks; Machine learning; Deep learning; Segmentation; Classification.