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注意TurkerNeXt:使用OCT图像检测双极性障碍的调查

Sermal Arslan¹, Mehmet Kaan Kaya¹, Burak Tasci²

¹Universal Eye Clinic, 23119 Elazig, Turkey.

Diagnostics (Basel, Switzerland)

|November 24, 2023

概括

一个新的深度学习模型,TurkerNeXt,通过光学连贯断层扫描 (OCT) 图像在检测双相情感障碍方面实现了100%的准确性. 这突出了OCT扫描作为这种情况的潜在生物标志物.

科学领域:

眼科医生眼科眼科
精神病学是一个精神病学.
计算机科学计算机科学

背景情况:

深度学习模型,特别是变压器,越来越多地用于计算机视觉.
注意力机制增强卷积神经网络 (CNN) 的图像分类.

研究的目的:

提出一种新的注意力卷积模型,用于使用光学连贯断层扫描 (OCT) 图像检测双相情绪障碍.
开发一种自动化OCT图像检测系统,用于治疗双相情感障碍.

主要方法:

一个新的关注CNN,TurkerNeXt被开发出来,灵感来自ConvNeXt和ResNet18架构.
该模型结合了类似于Swin变压器的修补操作和快捷操作,以防止消失的梯度.
为了研究,一个独特的OCT图像数据集被策划.

主要成果:

对于两个不同的OCT图像案例,TurkerNeXt模型在测试和验证组中都实现了100%的准确性.
数据集包括987个训练图像和328个测试图像.

结论:

一个新的OCT数据集和TurkerNeXt CNN模型已成功开发.
土耳其NeXt模型在双相情感障碍检测方面表现出色的分类性能.
经过CT图像显示了作为双相情感障碍生物标志物的显著潜力.

关键词:

注意TurkerNeXttt的注意事项海洋图像分类 OCT 图像分类发现生物标志物的生物标志物.双相情感障碍是双相情感障碍的一种疾病.

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