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Entropy Slicing Extraction and Transfer Learning Classification for Early Diagnosis of Alzheimer Diseases with sMRI

机译:SMRI早期诊断Alzheimer疾病的熵切割和转移学习分类

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Alzheimer's Disease (AD) is an irreversible neurogenerative disorder that undergoes progressive decline in memory and cognitive function and is characterized by structural brain Magnetic Resonance Images (sMRI). In recent years, sMRI data has played a vital role in the evaluation of brain anatomical changes, leading to early detection of AD through deep networks. The existing AD problems such as preprocessing complexity and unreliability are major concerns at present. To overcome these, a model (FEESCTL) has been proposed with an entropy slicing for feature extraction and Transfer Learning for classification. In the present study, the entropy image slicing method is attempted for selecting the most informative MRI slices during training stages. The ADNI dataset is trained on Transfer Learning adopted by VGG-16 network for classifying the AD with normal individuals. The experimental results reveal that the proposed model has achieved an accuracy level of 93.05%, 86.39%, 92.00% for binary classifications (AD/MCI, MCI/CN, AD/CN) and 93.12% for ternary classification (AD/MCI/CN), respectively, and henceforth the efficiency in diagnosing AD is proved through comparative analysis.
机译:阿尔茨海默病(AD)是一种不可逆的神经源性疾病,经历了记忆和认知功能的逐渐下降,其特征在于结构脑磁共振图像(SMRI)。近年来,SMRI数据在脑解剖变化的评估中发挥了重要作用,从而通过深网络预测广告的早期检测。现有的广告问题如预处理复杂性和不可靠性是目前的主要问题。为了克服这些,已经提出了一种模型(FEESCTL),其具有熵切片,用于特征提取和转移学习进行分类。在本研究中,试图在训练阶段选择最佳信息的MRI切片来选择熵图像切片方法。 ADNI数据集接受VGG-16网络采用的传输学习培训,用于将广告与正常的个人进行分类。实验结果表明,该模型的准确度为93.05%,二进制分类(AD / MCI,MCI / CN,AD / CN)和93.12%的三元分类的准确度(AD / MCI / CN)(AD / MCI / CN) “从此,因此通过比较分析证明了诊断诊断效率。

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