Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset

Machine learning and deep learning play a crucial role in identification of various diseases like neurological, skin, eyes, blood and cancers. The deep learning algorithms can be performed promising for prediction of Alzheimer's disease using MRI scans. Alzheimer disease becoming more common in the people (age 65 years or above). The disease becomes severe before the symptoms appear and causes brain disorder that cannot be cured by medicines and other therapies and treatments. So the early diagnosis is necessary to slow down its progression. Detection and prevention of Alzheimer disease is one of the active research area for the researchers nowadays. In this paper, we employed architectures of convoutional networks using freeze features extracted from source data set ImageNet for binary and ternary classification. All experiments were carried out using Alzheimer's disease national initiative (ADNI) data set consisting of MRI scans. The performance of proposed system demonstrates for classification of Alzheimer's disease versus mild cognitive impairment, normal controls versus mild cognitive impairment, and cognitive normal versus Alzheimer's disease. The results of proposed study show that VGG architecture outperforms the state-of-the-art techniques and number of architectures of conveNet (AlexNet, GoogLeNet, ResNet, DenseNet, Inceptionv3, InceptionResNet) in Alzheimer's disease detection, and achieves an identification test set accuracy of 99.27% (MCI/AD), 98.89% (AD/CN) and 97.06% (MCI/CN).

Automated summary

Machine learning and deep learning are crucial in identifying various diseases, such as Alzheimer's, with deep learning algorithms showing promising performance in prediction. This study utilized freeze features from ImageNet to construct convolutional networks, achieving high accuracy in Alzheimer's disease classification.