Alzheimer’s disease is becoming common in the world with the time. It is an irreversible and progressive brain disorder that slowly destroys the memory and thinking skills and, eventually, the ability to perform the simplest tasks. It becomes severe before the noticeable symptoms appear and causes brain disorder which cannot be cured by any medicines and therapies, however its progression can be slow down through early diagnosis. In this paper, we employed different CNN based transfer learning methods for Alzheimer disease classification. We have applied different parameters, and achieved remarkable accuracy on benchmark ADNI dataset. We have tested 13 differnt flavours of different pre-trained CNN models using a fine-tuned approach of transfer learning across two different domain on ADNI dataset (94 AD, 138 MCI and 146 NC). Comparatively, DenseNet showed better performance by achieving a maximal average accuracy of % 99.05. Significant improvement in accuracy has been observed as compared to previously reported works in terms of specificity, sensitivity and accuracy. The source code of propose framework is publicly available.

随着时间的流逝，阿尔茨海默氏病在世界上变得越来越普遍。它是一种不可逆的进行性脑部疾病，会慢慢破坏记忆和思维能力，最终破坏执行最简单任务的能力。它会在出现明显症状之前变得很严重，并引起无法通过任何药物和疗法治愈的脑部疾病，但是通过早期诊断，其进展可能会减慢。在本文中，我们采用了基于CNN的不同转移学习方法进行阿尔茨海默氏病分类。我们应用了不同的参数，并在基准ADNI数据集上实现了卓越的准确性。我们使用微调的转移学习方法在ADNI数据集（94 AD，138 MCI和146 NC）的两个不同域上测试了13种不同口味的不同的预训练CNN模型。比较，DenseNet通过达到％99.05的最大平均准确度显示出更好的性能。与以前报道的工作相比，在特异性，敏感性和准确性方面，已经观察到准确性的显着提高。proposal框架的源代码可公开获得。