Classification of Alzheimer’s Disease Using fMRI Data and Deep Learning Convolutional Neural Networks Saman Sarraf, Ghassem Tofighi Abstract— Over the past decade, machine learning tech- tant concepts about resting-state fMRI. First of all, as patients niques especially predictive modeling and pattern recognition do not need to do any task and there is no simulation, the in biomedical sciences from drug delivery system [7] to medical procedure will be more comfortable than a normal fMRI and imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire secondly, rs-fMRI data acquisition can be performed during issue and to solve complex medical problems. Deep learning a clinical scan and most researchers are interested in brain is power learning machine learning algorithm in classification network analysis and extraction from rs-fMRI data [8] [9] while extracting high-level features. In this paper, we used [11] [6] [2]. However, development of an assistant tool or convolutional neural network to classify Alzheimer’s brain from algorithm to classify fMRI data and more importantly to normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system recognize brain disorder data from healthy subjects has been in order to recognize the type disease from normal subjects or always clinicians? interests. Any machine learning algorithm to estimate the stage of the disease. Classification of clinical which is able to classify Alzheimer’s disease assists scientists data such as Alzheimer’s disease has been always challenging and clinicians to diagnose this brain disorder. In this work, and most problematic part has been always selecting the most the convolutional neural network (CNN) which is one of the discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully Deep Learning Network architecture is utilized in order to classified functional MRI data of Alzheimer’s subjects from classify the Alzheimer’s brains and healthy brains and to normal controls where the accuracy of test data on trained data produce a trained and predictive model. reached 96.85%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning II. BACKGROUND AND ALGORITHMS classification is most powerful method to distinguish clinical A. Data Acquisition and Preprocessing data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated For this study, Alzheimer‘s Disease (AD) patients and 15 systems. elderly normal control subjects (24 female and 19 male) with a mean age of 74.9 5.7 years were selected from ADNI I. INTRODUCTION dataset. The AD patients had MMSE over 20 reported by Alzheimer’s disease is a neurological, irreversible, pro- *ADNI and all normal participants were healthy and had gressive brain disorder and multifaceted disease that slowly no reported history of medical or neurological conditions. destroys brain cells causing memory and thinking skills loss, Scanning was performed on a Siemens Trio 3 Tesla MRI and ultimately the ability to carry out the simplest tasks. The scanner. Anatomical scans were acquired with a 3D MP- cognitive decline caused by this disorder ultimately leads to RAGE sequence (TR=2s, TE=2.63 ms, FOV=25.6 cm, 256 dementia. For instance, the disease begins with mild deteri- x 256 matrix, 160 slices of 1mm thickness). Functional oration and gets progressively worse in a neurodegenerative runs were obtained with an EPI sequence (150 volumes, type of dementia. Diagnosing Alzheimer’s disease requires TR=2 s, TE=30 ms, flip angle=70?, FOV=20 cm, 64 x 64 very careful medical assessments such as patients? history, matrix, 30 axial slices of 5mm thickness, no gap). The Mini Mental State Examination (MMSE) and physical and fMRI data were pre-processed using the standard modules arXiv:1603.08631v1 [cs.CV] 29 Mar 2016 neurobiological exam. In addition to those evaluations, rest- of FMRIB Software Library v5.0 [10]. The pre-processing ing state functional magnetic resonance imaging (rs-fMRI) steps for the anatomical data involved the removal of non- provides a non-invasive method to measure functional brain brain tissue from T1 anatomical images using the Brain activity and changes in the brain [13]. There are two impor- Extraction Tool. For functional data the preprocessing steps included: motion correction (MCFLRIT), skull stripping, and Data used in preparation of this article were obtained from spatial smoothing (Gaussian kernel of 5-mm FWHM). Low- the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database level noise was removed using a high-pass temporal filtering (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but (?=90.0 sec). The functional images were then aligned to did not participate in analysis or writing of this report. A complete the individual?s high-resolution T1-weighted scans, which listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp- were subsequently registered to the Montreal Neurological content/uploads/how to apply/ADNI Acknowledgement List.pdf Saman Sarraf was with the Department of Electrical and Computer Institute standard space (MNI152) using affine linear regis- Engineering, McMaster University, Hamilton, ON, L8S 4L8, Canada. tration and resampled at 2mm cubic voxels. The product of [email protected] preprocessing step was 45x54x45x300 images in which the Ghassem Tofighi is with the Electrical and Computer Engineer- ing Department, Ryerson University, Toronto, ON M5B 2K3 Canada. first 10 slices of each image were removed as they contained [email protected] no functional information. B. Deep Learning volume. Fully-Connected Layer (FC) layer computes the Hierarchical or structured deep learning is a modern class scores, resulting in volume of number of classes. As branch of machine learning that was inspired by human with ordinary Neural Networks and as the name implies, each brain. It has been developed based on complicated algorithms neuron in this layer will be connected to all the numbers that model high-level features and extract those abstractions in the previous volume [1] [4]. The Convolutional Layer from data by using similar neural network architecture but plays an important role in CNN architecture and is the much complicated. The neuroscientists discovered the ?neo- core building block in this network. The CONV layer’s cortex? which is a part of the cerebral cortex concerned with parameters consist of a set of learnable filters. Every filter is sight and hearing in mammals, process sensory signals by spatially small but extends through the full depth of the input propagating them through a complex hierarchy over time. volume. During the forward pass, each filter is convolved That was the main motivation to develop the deep machine across the width and height of the input volume, producing learning focusing on computational models for information a 2D activation map of that filter. During this convolving, representation that exhibit similar characteristics to that of the network learns filters that activate when they see some the neocortex [1] [4] [3]. specific type of feature at some spatial position in the input. 1) Convolutional Neural Networks (CNNs / ConvNets): Next, these activation maps are stacked for all filters along Convolutional Neural Networks which are inspired by hu- the depth dimension forms the full output volume. Every man visual system are similar to classic neural networks. entry in the output volume can thus also be interpreted as This architecture has been particularly designed based on an output of a neuron that looks at only a small region in the explicit assumption that raw data are two-dimensional the input and shares parameters with neurons in the same (images) that enables us to encode certain properties and activation map [1] [4] [3]. A Pooling layer is usually inserted also to reduce the amount of hyper parameters. The CNN in-between successive Conv layers in ConvNet architecture. topology utilizes spatial relationships to reduce the number Its function is to reduce (down sample) the spatial size of of parameters which must be learned and thus improves upon the representation in order to reduce the amount of network general feed-forward back propagation training. Equation ?? hyper parameters, and hence to also control overfitting. The shows how Error is calculated in the back propagation step Pooling Layer operates independently on every depth slice of where E is error function, y is the ith, jth neuron, x is the the input and resizes it spatially, using the MAX operation. input, l represent layer numbers w is filter weight with a and Recently, more successful CNN have been developed such as b indices, N is the number of neurons in a given layer and LeNet, AlexNet, ZF Net, GoogleNet, VGGNet and ResNet. m is the filter size. The major bottleneck of constructing ConvNet architectures is the memory restrictions of GPU [1] [4] [3]. N−m N−m l 2) Adoped LeNet-5: As you can see in the figure 3, @E X X @E @xij = (1) LeNet-5 was firstly designed by Y. LeCun et Al. [4] and @W @W l @W ab i=0 j=0 ij ab this famous network successfully classified digits and was applied to hand-written check numbers. The application of N−m N−m X X @E this network was expanded to more complicated problems = yl−1 (2) @xl (i+a)(j+b) and their hyper parameters adjusted for new issues. However, i=0 j=0 ij more developed versions of LeNet have been successfully In CNNs, small portions of the image (dubbed a local tested. In this work, we dealt with a binary but very compli- receptive field) are treated as inputs to the lowest layer cated binary classification of Alzheimer’s and Normal data.