Систематическое исследование методов обнаружения опухолей головного мозга на основе искусственного интеллекта
Ключевые слова:
обработка изображений, машинное обучение, глубокое обучение, гибридные методыАннотация
Мозг считается одним из наиболее эффективных органов, контролирующих организм. Развитие технологий сделало возможным раннее и точное обнаружение опухолей головного мозга, что существенно влияет на их лечение. Применение искусственного интеллекта значительно возросло в области неврологии. В этом систематическом обзоре сравниваются последние методы глубокого обучения (DL), машинного обучения (ML) и гибридные методы для обнаружения рака мозга. В статье дается оценка 36 недавних статей, посвященных этим методам, с учетом наборов данных, методологии, используемых инструментов, достоинств и ограничений. Статьи содержат понятные графики и таблицы. Обнаружение опухолей головного мозга в значительной степени опирается на методы машинного обучения, такие как метод опорных векторов (SVM) и метод нечетких C-средних (FCM). Рекуррентные сверточные нейронные сети (RCNN), плотная сверточная нейронная сеть (DenseNet), сверточные нейронные сети (CNN), остаточная нейронная сеть (ResNet) и глубокие нейронные сети (DNN) — это методы DL, используемые для более эффективного обнаружения опухолей головного мозга. Методы DL и ML объединяются для разработки гибридных методов. Кроме того, приводится краткое описание различных этапов обработки изображений. Систематический обзор выявляет нерешенные проблемы и будущие цели для методов на основе DL и ML для обнаружения опухолей головного мозга. С помощью систематического обзора можно определить наиболее эффективный метод обнаружения опухолей головного мозга и использовать его для улучшения.
Литература
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Опубликован
2023-05-22
Как цитировать
Кумар, С., Пилания, У., & Нандал, Н. (2023). Систематическое исследование методов обнаружения опухолей головного мозга на основе искусственного интеллекта. Информатика и автоматизация, 22(3), 541-575. https://doi.org/10.15622/ia.22.3.3
Раздел
Искусственный интеллект, инженерия данных и знаний
Copyright (c) Sanjeet Kumar, Unknown, Unknown
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