Deep learning based Alzheimer's disease early diagnosis using T2w segmented gray matter MRI

Abstract

Diagnosing Alzheimer's disease at early stage required an effective classification mechanism to differentiate mild cognitive impairment from cognitive normal and AD. In this paper, we used data set collected from ADNI and OASIS. Instead of using the whole volume of the MRI, high informative slices are selected using entropy. The selected slices are pre-processed by removing unwanted tissues using skull stripping algorithm and extracted gray matter using EICA. In this work, we used CNN model with inception blocks to extract deep features from the GM slices used to predict AD at early stage. The model avoids data leakage by considering all the slices of an MRI as a unit. The model trained with 80% of ADNI subject MRI volumes and tested with the remaining 20% subject MRI volumes, to provide great variance in training and testing data, the model further tested with OASIS data sets. 10-fold cross-validation is used to test the model without biasing. The model performance is evaluated using accuracy. The model achieves 98.73%, 100%, 93.72%, and 95.6% of accuracy for differentiating CN-MCI, CN-AD, AD-MCI and CN-MCI and AD. At 10-fold cross-validation it gives 92.92 ± 3%, 98 ± 2%, 90 ± 4% and 94.9 ± 2% accuracy to differentiate CN-MCI, CN-AD, AD-MCI, and CN-MCI-AD using ADNI. We further tested the model with 135 MRI volumes selected from OASIS data set, we achieved 92%, 91.76%, 88.23%, 81.48% of accuracy with CN-AD, MCI-AD, CN-MCI, and three-way classification. The model gives good accuracy and sensitivity of early AD Diagnosis.

AHANet: Adaptive Hybrid Attention Network for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Imaging

“…They have incorporated inception and residual blocks in the CNN model for deeper feature extraction for early AD classification. The gray matter segmentation from slices was performed using Enhanced Independent Component Analysis (ECIA) [ 29 ]. Raju et al designed a custom 3D CNN architecture to extract image features and adopted an SVM with an RBF kernel to perform AD classification [ 30 ].…”

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

“…Basheera et al [30] developed a modified Tresnet-based deep learning technique to recognize three stages NC, MCI, and AD. Initially, in the pre-processing stage, the skull-removing process was performed by the FMRIB Software Library software with batch processing.…”

A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images

“…In the second approach, the clinical biomarkers, including the level of tau and amyloid-beta proteins, are measured through the cerebrospinal fluid (CSF) or brain autopsy. Despite its acceptable performance, this approach usually requires invasive procedures for measurement, making it unpopular as a routine method for early diagnosis of AD (Basheera & Ram, 2021 ; Sun et al, 2021 ). In the third approach, neuroimaging modalities such as MRI, fMRI, and PET are used to show the structure and functionality of the brain.…”

“…In the third approach, neuroimaging modalities such as MRI, fMRI, and PET are used to show the structure and functionality of the brain. This method can provide large amounts of information in a short period of time; however, interpreting all the detailed information in images is relatively challenging for physicians (Basheera & Ram, 2021 ; Sun et al, 2021 ).…”