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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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联合分类和回归与深度多任务学习模型,使用传统的基于补丁提取用于脑疾病诊断.

Padmapriya K1, Ezhumalai Periyathambi1

  • 1Department of Computer Science and Engineering, RMD Engineering College, Chennai, Tamil Nadu, India.

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概括
此摘要是机器生成的。

一个新的深度多任务卷积神经网络 (DMTCNN) 准确地使用MRI扫描来诊断大脑疾病. 这种人工智能模型提高了诊断准确性和效率,以改善患者护理.

关键词:
脑部疾病 脑部疾病分类 分类 分类 分类.卷积神经网络是一种卷积神经网络.深度多任务卷积神经网络的神经网络磁共振成像技术 磁共振成像技术多任务学习多任务学习回归是一种回归.

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 神经学和诊断医学 神经学和诊断医学

背景情况:

  • 准确诊断大脑疾病对于有效的治疗计划和患者护理至关重要,磁共振成像 (MRI) 发挥着关键作用.
  • 当前的计算机辅助诊断方法通常依赖于MRI手工设计的功能,限制了它们的效率和适应性.
  • 深度多任务卷积神经网络 (DMTCNN) 拟用于克服脑疾病诊断中的这些局限性.

研究的目的:

  • 开发和验证一种有效的代方法,用于脑疾病诊断中的非平滑优化问题.
  • 介绍一个深度多任务卷积神经网络 (DMTCNN),用于联合回归和脑疾病的分类.
  • 通过多重诊断任务的合作学习,增强共享信息的利用,提高整体绩效.

主要方法:

  • 利用深度多任务卷积神经网络 (DMTCNN) 进行与大脑疾病相关的同时回归和分类任务.
  • 使用边缘探测器,特别是Canny边缘探测器,用于预处理MRI图像.
  • 在多任务学习 (MTL) 框架内,使用卷积神经网络 (CNN) 从图像补丁中提取了歧视性解剖特征.

主要成果:

  • DMTCNN模型在关键性能指标上显著改善,包括特异性 (94.18%),敏感性 (93.19%),准确性 (96.97%) 和F1得分 (94.18%).
  • 实现了22.76%的根平均平方误差 (RMSE) 和4.875毫秒的执行时间,展示了高效率.
  • 验证了DMTCNN能够准确分类不同类型的大脑疾病的能力,其性能优于最先进的技术.

结论:

  • 拟议的DMTCNN是使用MRI进行脑部疾病的计算机辅助诊断的有效和高效方法.
  • 多任务学习提高了模型的概括性和诊断准确性,即使具有有限的标记数据.
  • 与现有方法相比,DMTCNN在精确识别各种脑部疾病方面表现优异.