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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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使用先进的深度学习技术自动化癌症诊断,用于多种癌症图像分类.

Yogesh Kumar1, Supriya Shrivastav2, Kinny Garg3

  • 1Department of Computer Science and Engineering, School of Technology, PDEU, Gandhinagar, Gujarat, 382426, India.

Scientific reports
|October 24, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了使用深度学习模型进行人工智能驱动的癌症检测. DenseNet121实现了99.94%的准确性,证明了AI的准确性.

关键词:
癌症检测 癌症检测深度学习是一种深度学习.医学成像医学成像多种癌症的诊断多种癌症的诊断消除噪音 消除噪音 消除噪音辐射疗法 辐射疗法

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 癌症是全球主要的死亡原因,需要早期检测.
  • 传统的方法往往是侵入性的和耗时的.
  • 需要有效和准确的自动化癌症检测解决方案.

研究的目的:

  • 评估用于自动癌症检测的深度学习模型.
  • 为了比较各种卷积神经网络 (CNN) 的性能.
  • 确定用于多种癌症图像分析的最有效的AI模型.

主要方法:

  • 使用了深度学习模型,包括DenseNet121,DenseNet201,Xception,InceptionV3,MobileNetV2,NASNetLarge,NASNetMobile,InceptionResNetV2,VGG19和ResNet152V2. 这三种学习模型.
  • 应用图像细分和轮特征提取 (周边,面积,epsilon).
  • 在七种癌症类型的图像数据集上评估模型:大脑,口腔,乳腺,脏,急性淋巴细胞白血病,肺和结肠以及宫癌.

主要成果:

  • DenseNet121实现了最高的验证准确率99.94%,损失为0.0017.
  • 在训练 (0.036056) 和验证 (0.045826) 两方面,DenseNet121显示出最小的根平均平方误差 (RMSE).
  • 该研究成功地将DenseNet121确定为表现最佳的模型.

结论:

  • 基于人工智能的技术,特别是深度学习,显著提高了癌症检测的准确性.
  • DenseNet121显示出在多种癌症类型中实现自动检测的特殊能力.
  • 这项研究强调了人工智能在改善早期癌症诊断和患者治疗结果方面的潜力.