A multilayered framework for diagnosis and classification of Alzheimer's disease using transfer learned Alexnet and LSTM

Alzheimer's disease (AD) is the most frequent type of dementia that has no effective cure, except early discovery and treatment that may help patients to include successful years in patient's lives. Currently, mini-mental state examination (MMSE) score and manual examination of magnetic resource imaging (MRI) scan along with machine learning techniques are used to diagnose the disease; however, they possess certain accuracy limits. Therefore, this paper proposes a deep learning-based multilayered framework for AD classification using transfer learned Alexnet and LSTM for multiclass and binary classification of MR images. However, the deep learning models used in the current study necessitate a large training dataset to produce better outcomes. As a result, this work also utilizes generative adversarial network (GAN) as a data augmentation tool to improve the classification results and further to solve the problem of overfitting. The study uses Alzheimer's disease neuroimaging initiative (ADNI) dataset of 60 AD, 73 mild cognitive impairment (MCI) and 67 cognitively normal (CN) patients from which 2 D MR image scans are extracted. Furthermore, the proposed method achieved the classification accuracy on AD-CN at 98.13%, AD-MCI at 99.38% and CN-MCI at 99.37%, respectively. Also, the multiclass classification shows the promising accuracy of 96.83% for the proposed framework. Finally, the proposed model's performance is compared to other state-of-the-art techniques and the experimental results show that the proposed model outperforms in terms of accuracy, sensitivity and hypothesis testing.