СРАВНИТЕЛЬНЫЙ АНАЛИЗ МОДЕЛЕЙ U-NET, U-NET++, TRANSUNET AND SWIN-UNET В ЗАДАЧЕ СЕГМЕНТАЦИИ РЕНТГЕН-СНИМКОВ ЛЕГКОГО

Сегментация медицинских изображений является широко используемой задачей в обработке медицинских изображений. Использование сегментации в медицине позволяет получить местоположение и размер необходимой сущности. Существует несколько важных факторов при выборе модели. Во-первых, модель должна обеспечивать точное предсказание маски. Во-вторых, модель не должна требовать большого объема вычислительных ресурсов. Наконец, следует учесть распределение между ложноположительными и ложноотрицательными предсказаниями. Мы предоставляем сравнительный анализ четырех моделей глубокого обучения: базовой U-Net и ее расширений U-Net++, TranUNet и Swin-UNet для сегментации легких по рентгеновским снимкам на основе обучаемых параметров, DICE, IoU, расстояния Хаусдорфа, точности и полноты. Модели CNN с наименьшим количеством параметров показывают самые высокие показатели DICE и IoU по сравнению с моделями с большим количеством параметров на ограниченном по размеру наборе данных. Согласно результатам эксперимента, представленным в статье, U-Net имеет максимальные DICE, IoU и точность. Это делает модель наиболее подходящей для сегментации медицинских изображений. SwinU-Net – модель с минимальным расстоянием Хаусдорфа. U-Net++ имеет максимальную полноту.

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