Alzheimers Disease Detection Using Cnn And Vision Transformation

Dr.P. Jeevana Jyothi, G. N. Sri, Ch. Annie Anuhya, B. Akhila, B. Bhanu
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Abstract

Alzheimer’s disease is a brain related issue which effects the mental stability of a person. It degrades the thinking capability and targets the memory of a person.The person who is effected with the Alzheimer’s finds difficult to even remember simple daily things[8].Even the very recent or latest event is also difficult for them to remember or keep track of.TheAlzheimer’s disease is challenging one because there is no treatment for the disease. This disease is currently ranked as the seventh leading cause of death in the United States among older adults.There is no permanent cure or treatment for this. Thus, if the disease is predicted earlier, the progression or the symptoms of the disease can be slow down. In this paper we intend to create a model that detects Alzheimer disease using GAN and CNN.GAN can be adopted to fulfil the role of data augmentation. GANs are generative models: they create new data instances that resemble your training data. Classification process can be fulfilled by using the CNN model to the data for improving the efficiency and to ensure higher accuracy.
基于Cnn和视觉变换的阿尔茨海默病检测
阿尔茨海默病是一种与大脑有关的疾病,它会影响一个人的精神稳定性。它会降低人的思维能力,并以人的记忆力为目标。患有阿尔茨海默病的人甚至很难记住简单的日常事物[8]。即使是最近或最近的事件对他们来说也很难记住或跟踪。阿尔茨海默病是一种具有挑战性的疾病,因为这种疾病没有治疗方法。这种疾病目前被列为美国老年人死亡的第七大原因。没有永久性的治疗方法。因此,如果早期预测疾病,疾病的进展或症状可以减缓。在本文中,我们打算使用GAN和CNN创建一个检测阿尔茨海默病的模型。可以采用GAN来完成数据增强的作用。gan是生成模型:它们创建类似于训练数据的新数据实例。通过对数据使用CNN模型来完成分类过程,提高了效率,保证了更高的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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