‫ م‬2021 ‫الربانمح البحثي الصيفي‬
‫جامعة امللك فهد للبرتول واملعادن‬
Magnetic resonance imaging (MRI) in early
diagnosis and classification of AD using
convolutional neural network
Maryam Faisal Altuwaijri .
E-mail: maryamfaisal033@gmail.com
29-july-2021
Abstract
Although advancements in diagnostic imaging such as
magnetic resonance imaging (MRIs) have led to a greater
understanding of the diagnosis and treatment of Alzheimer's
Disease (AD), medical professionals are still required to
analyze the images which is a time-consuming and errorprone process. With the help of neural network models,
diagnoses can be reached more accurately and efficiently. In
this study I use well-known CNN approaches to determine
the most accurate at diagnosis and performing multiclass
classification of brain MRI scans of AD patients [1 I also
perform multilevel classification of Alzheimer’s disease ie;
Mild Demented, Moderate Demented, Non-Demented and
Very Mild Demented using CNN with InceptionV3 using
Fastai. This approach results in 94% predictive accuracy
which means a significant increase in accuracy. compared to
previous studies and clearly demonstrates the effectiveness of
the proposed methods.
keyword: Machine learning (ML), Alzheimer's disease (AD),
convolutional neural network (CNN) , Magnetic resonance
imaging
(MRI).
1.Introduction
Alzheimer’s disease (AD) is a type of dementia disease that is
common in older ages. a terminal illness that is characterized by
progressive memory loss, disorientation, and pathological markers,
including senile plaques and neurofibrillary tangles in the brain.
Most often, AD is diagnosed in people over 65 years of age
although the less-prevalent early-onset Alzheimer's disease can
occur in much younger people. These attacks are sufficiently
intense to affect the daily social and professional lives of patients.
Today, in the absence of a reliable diagnosis and effective curative
treatments, fighting this disease is becoming a real public health
issue, prompting research to consider non-drug techniques.
It is named after German neurologist Aloïs Alzheimer, who in 1907
first described the symptoms as well as the neuropathological
features of the disease, such as amyloid plaques and tangles in the
brain.
In this research, you will find the answers to the following
questions:
-What’s the best ML (CNN, RF, SVM…) model for detection AD
from MRI?
-What are the exist methods used to diagnosis AD using
MRI?
This research paper contains 5 sections. We review previous
knowledge and related works in the next section. Then we
elaborate on the research methodology. Experiments and the
results generated by them will be described in section 4. Finally,
the research concludes in section 5 with a future work plan.
2.Related works
As early as 1999, researchers have been employing various machine
learning methods to detect AD onset from MRI data alone. Researchers
began by establishing biomarkers to be evaluated. A biomarker is a
quantifiable indicator of a biological state. Hippocampal volume has been
a key biomarker that doctors have used in the past, and it has shown high
diagnostic accuracy for AD. However, hippocampal volume alone is
considered clinically insufficient as a predictor of progression from mild
cognitive impairment (MCI) to AD. Hence there is a need to look at more
features, and recent research has shown that important changes in many
other cortical regions also occur. Given the spatial features in the MRI
scans, machine learning and deep learning methods tend to be good at
aggregating such features for classification.
Images are one of the data expression methods in photography
form. It consists of small elements called pixels that have a specific
value and position. The techniques of handling an image using
computer algorithms are known as Image Processing. Image
processing is an important part of the many images' analysis [2].
Image classification plays an important part in the field of medicine
and teaching research. Radiologists and medical experts see tumor
detection and extraction as such a tedious and arduous task to
perform. The analysis and accuracy depend on their experience
alone. However, there is a limit to consistent analysis and accuracy
of both experienced and inexperienced medical expert. In fact, the
task becomes increasingly burdensome when there are abundant
data present to be analyzed. Hence, the use of cutting-edge
technology comes in hand to overcome these challenges [3].
Title
date of
publication
data
model
accuracy
Diagnosis of Alzheimer’s Disease
Based on Structural MRI Images
(2018)
ADNI
SVM, IVM,
and RELM
57.40
,55.50
,61.20
Classification of Structural MRI Images in
Alzheimer’s Disease from the Perspective
of Ill-Posed Problems
Automated Classification of Alzheimer’s
Disease Based on MRI Image Processing
using Convolutional Neural Network
(CNN) with AlexNet Architecture
Comparing the Architecture and
Performance of AlexNet, Faster R-CNN,
and YOLOv4 in the Multiclass
Classification of Alzheimer Brain MRI
Scans
(2012)
ADNI
RLR, LRC, SVM
91,90,88
(2020)
Kaggle
AlexNet
95
-
Kaggle
AlexNet.
Faster R-CNN,
YOLOv4
99,84,99
Table1. Recently published scientific papers
One work employed a Deep Neural Network containing
autoencoders to combine features from Region of Interest (ROI)
in PET and MRI scans. Plant et al. (2010) used feature selection to
achieve 92% accuracy in a binary classification of AD . Their paper
demonstrates that principal component analysis, independent
component analysis, structural equation modeling, and support
vector machines have recently shown promise analyzing structural
MRI to detect spatial patterns of atrophy associated with AD.
ROI-based methods such as this can extract representative features
and partly reduce the feature dimensions, but usually the ROIs are
too empirical as they are based on qualitative observation. This is
not sufficient to capture the more granular features associated with
AD. An additional method proposed was to capture successive
slices from MRI scans because it was deemed that each slice covers
the significant areas for dementia detection. Wang et al. (2018)
used DenseNet and ensemble methods to classify the entire 3D
MRI scan, leading to a new state of the art three class classification
(Alzheimer versus Mild Cognitive Impairment versus Cognitively
Normal) accuracy of 97% [4].
3.Methodology
To write this research we follow ML methodology which start by
collecting the data then preprocess the data and then we build the
CNN that going to classify the 4 classes then training and testing
the model .
Data
Data pre-
collection
processing
Construct the
CNN
architecture
Training &
Testing the
model .
Figure1. Block diagram of the proposed methodology for Magnetic resonance imaging (MRI)
in the diagnosis and classification of AD using convolutional neural network.
The primary method of diagnosis is analysis of MRI images of the
patient's brain. Our proposed model is a convolutional neural
network (CNN). The model has several layers performing four
basic operations - convolution, batch normalization, rectified
linear unit, and pooling. The layers in the model follow a particular
connection pattern known as dense connectivity, where each layer
is connected to every other layer. For final classification, there is a
SoftMax layer with four different output classes: nondemented,
very mild, mild and moderate AD. This data augmentation
technique increases the number of samples in the training data set.
The size of each patch is 176*176 [5].
The following steps were taken in Figure1 to prepare the images for
analysis, select a base model, and tune hyperparameters. Several
helper functions were built to simplify tasks for reuse throughout the
analysis.
Data collection
The data utilized in the completion of this project was obtained
from a publicly available database on Kaggle. 6400 segmented MRI
images of the brain from an axial view were labeled and divided
into four classifications: NonDemented, VeryMildDemented,
MildDemented, and ModerateDemented. These images were then
divided into the training and test set in a 4:1 ratio, respectively. It
is important to note that the number of images per class were
unbalanced, especially in regards to the ModerateDemented,
which had only 52 images in the training and 12 images in the
testing sets.
Classes
The number
NonDemented
3200
VeryMildDemented
2240
MildDemented
896
ModerateDemented
64
Table1. The number of images in each classification for the training and test
set.(total:6400)
Figure 2. Four different classifications of Kaggle brain MRI dataset (a) non-Demented (b) Very
Mild Demented (c) Mild Demented (d) Moderate Demented.
There are a number of libraries required for this analysis, Keras is used
to perform grid-search to find optimal hyperparameters and split-folders
is used to split folders of images into test, train and validate folders with
stratification for classes [7].
Data pre-processing
The number of images per class was largely unbalanced, with the
ModerateDemented images being vastly underrepresented (total:
64 images) in comparison to the MildDemented (total: 3200). So,
we decided to process the image and rearrange it, so that each
class contains 2,3200 images, with a total of 12800 images by
Image random flipping and image random zooming.
Image generators from TensorFlow were used to feed images
directly to our model from the image directories. The images
were grayscale and needed to be converted to color to use with
pre-trained models.
Image Transformations
•
•
•
scale images
convert to RGB
set seed for reproducibility
Construct the CNN architecture
Flatten
MaxPooling
176,176,3
MRI image
176,176,16
88,88,16
Sequential
11,11,128
Dropout
Sequential
Denese
5,5,256
None,4
None,6400
None,64
00
CNN architecture , For more details, click on Here
Convolutional neural networks and deep learning
generally have been contributing immensely to various
innovations such as image classifications and segmentation,
as well as object detection and recognition research on
computer vision. These techniques have been successfully
implemented in automation task and eliminating the
tedious work of handcrafted engineering gradually. CNN
and deep learning were trying to mimic the human visual
cortex system in structure and operation by adopting a
hierarchical layer of feature representation. Multilayer
CNN model it possible to train different image features
automatically, and enabled CNNs to perform better than
hand crafted-feature techniques.
The earlier convolutional blocks in the model have learned
to recognize very low-level features that are common to
all-natural images so they will already be optimized. The
convolutional layers at the end will learn higher level
features that are more specific to the dataset being used so
these will likely need more optimization during training.
We also had originally used an Adam optimizer, which is an
adaptive moment optimizer. This has the benefits of being
straightforward to implement, is computationally efficient,
and its hyper-parameters have intuitive interpretation,
typically requiring little tuning. However, we found through
research that an Adam optimizer might not be well suited for
my needs as they function best for data which has nonstationary targets or non-convex optimization problems. Our
classification problem has stationary targets and a generally
convex solution [6].
Training & testing the model .
After trying more than one model, and we got the best result using
CNN with InceptionV3 which is the best ML model for detection
AD from MRI and following the best methods.
The training & testing took us about an hour and 54 minutes,
CPU: 393%, Disk: 2.7GB (Max 73.1GB), RAM: 12.2GB (Max
16GB).
4. Results and Discussion
A total of 6400 images was used out of which 896 Mild demented,
64 Moderate demented, 3200 Non demented, 2240 very mild
demented. We measured the performance of our algorithm using
1013 test images out of which 139 Mild demented, 10 Moderate
demented, 530 Non demented, 334 very mild demented. We
obtained an accuracy of 94.8% on the test data, with a loss of ~0.18
This is an exceptional result that we were very proud of. We used
a confusion matrix to see which classes, if any, were being
misclassified more. We can see in the matrix that there is almost no
misclassification occurring at all, with the few that do occur
happening mostly between the “Normal” brains and the “Very
Mildly Demented” brains. This result makes sense intuitively as the
distinction between normal and mild dementia is the smallest
relative to each of the other classes. which is remarkable
achievement when compared to existing state of art. InceptionV3
enabled us to give such a good performance for the four way
classification of Alzheimer's disease. The confusion metrics was
computed as given in the figure3 using which Precision, Support,
Recall and F1 score was computed for further analysis of the
classification task a given in. [8]
Figure3. Confusion Matrix of 4 way classification.
NonDemented
VerMildDemented
MildDemented
ModerateDemented
Micro avg
Macro avg
Weighted avg
Samples avg
Pprecision
0.96
1.00
0.93
0.90
0.95
0.95
0.95
0.95
Recall
1.00
1.00
0.89
0.91
0.95
0.95
0.95
0.95
F1-score
0.98
1.00
0.91
0.90
0.95
0.95
0.95
0.95
Support
639
635
662
624
2560
2560
2560
2560
Table2. Performance measures.
For the training process the learning was chosen which helped to attain
smooth exponentially decreasing curve for training and validation losses and
an exponentially increasing curve for training and testing accuracy as shown
in Figure 4. FASTAI helped to finish the training process in number of
epochs.
Figure 4. Graph of training and testing accuracy with epochs(loss).
Our current methodology performs inference over the entire MRI
scan rather than only specific regions in the brain. The excellent
results produced by this method has supported the hypothesis that
features of Alzheimer’s diseases are not discerned in specific brain
regions but rather aggregate and hidden features present
throughout the brain.
We have demonstrated an efficient approach to Alzheimer’s Disease
diagnosis using brain MRI data analysis. While the majority of the
existing research works focuses on binary classification, our model
provides significant improvement for multi-class classification. Our
proposed network can be very beneficial for early-stage Alzheimer’s
Disease diagnosis. Though the proposed model has been tested only
on Alzheimer’s Disease dataset, we believe it can be used
successfully for other classification problems of the medical
domain.
The experiment was evaluated using 4 different metrics in order to
ascertain the performance of the models. The following
performance metrics were used namely; accuracy, F1- score,
precision and recall.
5.Conclusion
We can see from the confusion matrix that the model does best at
identifying normal and very-mild MRIs and poorest identifying
moderate cases. This makes sense given the small number of
moderate examples in the dataset. Adding model capacity, searching
for optimal hyperparameters and adding data augmentation resulted
better performance on the test dataset.
In this Work, we used the basic InceptionV3 model with Fastai to
classify Alzheimer's from MRI images. The proposed work was able
to give an accuracy of 94.8% on test data with very small
misclassifications on normal and very mild demented. Future work
includes using data from other modalities like PET, fMRI to improve
the performance. I also would like to explore feature extraction on
select brain regions. More data from more patients are also required
in the future to ensure the accuracy of the model stays consistently
high. Several methods in literature are used to address the problem
of limited training data which I would like to explore more in the
future.
I acknowledge our gratitude to the use of Alzheimer’s disease (4
class of images) and the data derived from Kaggle [9] database. I
also thank the referees for their useful suggestions Dr.Imane
boudellioua & Miss.Leina abouhagar. This research was supported
by King Fahad university of petroleum and minerals.
References:
[1] Comparing the Architecture and Performance of AlexNet, Faster RCNN, and YOLOv4 in the Multiclass Classification of Alzheimer Brain
MRI Scans
[2],[3] Multi-classification of alzheimer disease on magnetic resonance
images (MRI) using deep convolutional neural network (DCNN)
approaches
[4],[6] Early Detection of Alzheimer’s Disease Through Machine Learning
in MRI Scans
[5] Machine Learning Theory and Applications for Healthcare
[7] Alzheimer's
Multi-class Classification
[8] Deep Learning Based Multilevel Classification of Alzheimer’s Disease using
MRI Scans
[9] Alzheimer's Dataset ( 4 class of Images)