Abstract
We compare approaches for the automatic detection of pathological changes in brain MRI images that are visible to the naked eye. We analyse multi-stage approaches based on deep learning and threshold processing. A convolutional neural network was formed, a classifier was built based on the use of an ensemble of decision trees, and an algorithm was created for multi-stage image processing. Because of experimental studies, it was found that the most effective method for recognizing images of magnetic resonance imaging is an approach based on an ensemble of decision trees. With its help, 95 % of the images from the test sample were classified correctly. At the same time, using the convolutional neural network, it was possible to classify correctly all images containing the area of pathological changes. The data obtained can be used in practice for the diagnosis of brain diseases, for automating the processing of a large number of studies of magnetic resonance imaging.
Highlights
We compare approaches for the automatic detection of pathological changes in brain MRI images that are visible to the naked eye
We analyse multi-stage approaches based on deep learning and threshold processing
A convolutional neural network was formed, a classifier was built based on the use of an ensemble of decision trees, and an algorithm was created for multi-stage image processing
Summary
Для решения задачи определения факта присутствия на изображении МРТ головного мозга видимых патологических изменений предлагается классифицировать области, выделенные с помощью алгоритма, описанного в параграфе 1, вместо применения к ним эвристических правил (6). Отношение радиуса Ro наименьшей окружности, описанной вокруг контура, к высоте изображения
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