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Updated: Jun 25, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

优化的编码器-解码器级联深卷积网络用于叶病图像分割的叶病图像分割.

David Femi¹, Manapakkam Anandan Mukunthan¹

¹Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.

Network (Bristol, England)

|May 22, 2024

概括

此摘要是机器生成的。

一个新的优化深度编码解码级联网络 (ODEDCNet) 模型使用Dingo优化算法 (DOA) 进行精确的植物叶病细分和分类. 这种方法通过实现精确的,自动化的疾病检测来提高全球粮食安全.

关键词:

在DEDCNet中,我们可以使用DEDCNet.叶病的分类叶病的分类深度学习是一种深度学习.丁戈优化器丁戈优化器剥削剥削剥削使用勘探勘探勘探是一个过程.这就是超参数的超参数.

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科学领域:

农业科学农业科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

深度学习 (DL) 自动识别植物疾病,这对粮食安全至关重要.
深度编码解码级联网络 (DEDCNet) 精确地细分了病变的叶子区域.
DEDCNet模型培训需要仔细选择超参数以获得稳定性.

研究的目的:

提出一个优化的DEDCNet (ODEDCNet) 模型,用于增强叶病图像细分.
引入Dingo优化算法 (DOA) 以实现最佳的DEDCNet超参数选择.
提高自动化植物疾病诊断的准确性和效率.

主要方法:

开发了ODEDCNet集成Dingo优化算法 (DOA) 进行超参数调整.
DOA模仿了丁哥的食行为,以便在搜索空间中有效地进行探索和开发.
训练有素的DEDCNet与优化的超参数进行细分,其次是CNN和SVM进行分类.

主要成果:

ODEDCNet实现了高精度:97.33%的PlantVillage和97.42%的贝特利叶数据集.
获得了优秀的性能指标,包括回忆,F-score,Dice系数和精度.
对各自的数据集实现了0.07秒和0.06秒的快速处理时间.

结论:

拟议的ODEDCNet模型与DOA在叶病细分和分类方面表现出卓越的表现.
DOA有效地优化了DEDCNet的超参数,从而提高了准确性和稳定性.
这种自动化方法对植物疾病的早期检测和全球粮食安全作出了重大贡献.