Earlier Detection of Alzheimer’s Disease Using 3D-Convolutional Neural Networks

Abstract

The prediction of mild cognitive impairment or Alzheimer’s disease (AD) has gained the attention of huge researchers as the disease occurrence is increasing, and there is a need for earlier prediction. Regrettably, due to the high-dimensionality nature of neural data and the least available samples, modelling an efficient computer diagnostic system is highly solicited. Learning approaches, specifically deep learning approaches, are essential in disease prediction. Deep Learning (DL) approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging. A novel 3D-Convolutional Neural Network (3D-CNN) architecture is proposed to predict AD with Magnetic resonance imaging (MRI) data. The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification. The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative (ADNI) data. The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches. The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive. Keras’ experimentation is carried out, and the model’s superiority is compared with various advanced approaches for multi-level classification. The proposed model gives better prediction accuracy, precision, recall, and F-measure than other systems like Long Short Term Memory- Recurrent Neural Networks (LSTM-RNN), Stacked Autoencoder with Deep Neural Networks (SAE-DNN), Deep Convolutional Neural Networks (D-CNN), Two Dimensional Convolutional Neural Networks (2D-CNN), Inception-V4, ResNet, and Two Dimensional Convolutional Neural Networks (3D-CNN).