1. Dr . AMBEDKAR INSTITUTE OF TECHNOLOGY
(An Autonomous Institute, Affiliated To Visvesvaraya Technological University, Belagavi, Accredited By
NAAC, With ‘A’ Grade)
Near Jnana Bharathi Campus, Bengaluru – 560056
Department Of Computer Science And Engineering
Major Project On
“CLASSIFICATION OF BRAIN TUMOUR USING MRI SCANS”
Under The Guidance Of
Mrs. Mamatha S K
Assistant Professor, Dept. Of CSE,
Dr.AIT , Bengaluru – 56.
Submitted by:
Chandrakala P(1DA17CS036)
Devika D(1DA17CS042)
Aishwarya Singhe ( 1DA18CS400)
Chandana K (1DA17CS034)
4. ABSTRACT
Automated defect detection in medical imaging has become the emergent field in
several medical diagnostic applications.
Automated detection of tumour in MRI is very crucial as it provides information
about abnormal tissues which is necessary for planning treatment.
The conventional method for defect detection in magnetic resonance brain images is
human inspection.
It is tedious and time consuming task. This method is impractical due to large
amount of data. Hence, trusted and automatic classification schemes are essential to
prevent the death rate of human.
The MRI brain tumour detection is complicated task due to complexity and variance
of tumours. The MRI deals with the complicated problem of brain tumour detection.
In this project, we propose the machine learning algorithms to overcome the
drawbacks of traditional classifiers where tumour is detected in brain MRI using
machine learning algorithms.
5. INTRODUCTION
In the field of Medical Image Analysis, research on Brain tumors is one of the most
prominent ones Primary brain tumors occur in around 250,000 people a year
globally, making up less than 2% of cancers.
Tumor is the unusual growth of the tissues. A brain tumor is a quantity of
unnecessary cells growing in the brain or central spine canal.
In Order to detect the brain tumor of a patient we consider the data of patients like
MRI images of a patient’s brain. Here our problem is to identify whether there are
tumors cells or not. If present, it classifies the type of tumor.
In this project, we Estimate the brain tumor severity using Convolutional Neural
Network algorithm which gives us accurate results.
6. SCOPE OF THE PROJECT
• This project focuses on medical application to find the occurrence of Brain tumor.
• To significantly decrease the death rate of humans.
• To reduce the cost of the machine for brain tumor identification.
• To reduce the time required to get the optimal solution.
7. OBJECTIVE
• Millions of deaths can be prevented through early detection of brain tumour. Brain
tumour detection using MRI may increase patient’s survival rate.
• In MRI, tumour is shown more clearly that helps in the process of further treatment.
This work aims to detect tumour at an early phase.
• The important feature of MRI is that it does not produce any harmful radiation also it
is reliable and has fast detection and classification of brain cancer.
• The conventional method of brain tumor detection involves human inspection and
it’s tedious and time consuming, hence an automated detection methods are
developed as it would save radiologist time and obtain a tested accuracy and save
millions of lives.
8. PROBLEM STATEMENT
• The aim of the paper is tumor identification in brain MRI images.
• The main reason for detection of brain tumors is to provide aid to clinical diagnosis.
• The aim is to provide an algorithm that guarantees the presence of a tumor by
combining several procedures to provide a fool-proof method of tumour detection in
MRI brain images.
• The methods utilized are filtering, contrast adjustment, negation of an image, image
subtraction, erosion, dilation, threshold, and outlining of the tumour.
• The focus of this project is MRI brain images tumour extraction and its
representation in simpler form such that it is understandable by everyone
9. LITERATURE SURVEY
Today’s recent medical imaging research faces the challenge of detecting brain
tumor through Magnetic Resonance Images (MRI).
Broadly, to produce images of soft tissue of human body, MRI images are used by
experts. For brain tumor detection, image segmentation is required.
Mechanizing this process is a tricky task because of the high diversity in the
appearance of tumor tissues among different patients and in many cases similarity
with the usual tissues.Physical segmentation of medical image by the radiologist is a
monotonous and prolonged process.
MRI is a highly developed medical imaging method providing rich information
about the person soft-tissue structure. There are varied brain tumor recognition and
segmentation methods to detect and segment a brain tumor from MRI images.
10. EXISTING METHODS
Fusion based : Overlapping the train image of the victim over a test image of same age
group, thereby detecting the tumor.
Demerits :
The overlapping creates complexity due to different dimensions of both images.
Time consuming process.
Canny Based : To overcome the problem of detecting the edges, the better way is the
use of Canny based edge detection.
Demerits :
Not support color images.
This leads to increase in time to reach the optimal solution.
11. MRI SCANS
• Magnetic resonance imaging (MRI) uses large magnet and radio waves to look at
organs and structures inside your body. It measures the small changes in blood flow
that occur with brain activity.
• Health care professionals use MRI scans to diagnose a variety of conditions, from
torn ligaments to tumours.
• MRI’s are very useful for examining the brain spinal chord.
• The following is the image of an MRI Scan of a brain
14. CLASSIFICATION OF TUMORS
Tumors can be classified based on the region of the brain the tumor cells grow.
We are mainly concerned about three types of tumors in this project.
They are:
• Glioma Tumour
• Meningioma Tumour
• Pituitary Tumour
15. GLIOMA TUMOUR
Glioma is a type of tumour that occurs in the brain
and spinal cord.
Gliomas begin in the gluey supportive cells (glial
cells) that surround nerve cells and help them
function.
A glioma can affect your brain function and be
life-threatening depending on its location and rate
of growth.
The type of glioma you have helps determine your
treatment and your prognosis.
In general, glioma treatment options include
surgery, radiation therapy, chemotherapy, targeted
therapy and experimental clinical trials.
16. MENINGIOMA TUMOUR
A meningioma is a tumour that arises from the
meninges the membranes that surround your brain
and spinal cord.
Their effects on nearby brain tissue, nerves or
vessels may cause serious disability. Meningiomas
occur more commonly in women and are often
discovered at older ages, but may occur at any age.
A meningioma is a primary central nervous system
(CNS) tumor. This means it begins in the brain or
spinal cord.
17. PITUITARY TUMOUR
Pituitary tumours are abnormal growths that
develop in your pituitary gland.
Some pituitary tumours result in too much of the
hormones that regulate important functions of your
body.
Some pituitary tumours can cause your pituitary
gland to produce lower levels of hormones.
There are various options for treating pituitary
tumours, including removing the tumour,
controlling its growth and managing your hormone
levels with medications.
18. HARDWARE REQUIREMENTS
• Microsoft Windows 7/8/10 (32-bit or 64-bit)
• 3 GB RAM minimum, 8 GB RAM recommended
• 2 GB of available disk space minimum, 4 GB recommended
• 1280 x 800 minimum screen resolution
19. SOFTWARE REQUIREMENTS
• Python 3.7
• Python (Idle or jupyter notebook)
• OS (windows 64 bit 7/8.1/10)
• Opencv
• Tensorflow (1.5 version)
20. METHODOLOGY
In any project, planning the course of action is very important. Selecting proper
methodologies decide the outcomes. The following are the steps that are processed
inorder to get the desired outcome:
• Data pre-processing
• Image Pre-processing
• Segmentation
• Feature Extraction
• classification
21. DATA PRE-PROCESSING
For every image, the following pre-processing steps were applied:
Crop the part of the image that contains only the brain (which is the most important
part of the image).
Resize the image to have a shape of (150, 150, 3)=(image_width, image_height,
number of channels): because images in the dataset come in different sizes. So, all
images should have the same shape to feed it as an input to the neural network.
Apply normalization: to scale pixel values to the range 0-1.
22. IMAGE PRE-PROCESSING
• Our pre-processing includes rescaling, noise removal to enhance the image, applying
Binary Thresholding and morphological operations like erosion and dilation, contour
forming.
• In the first step of pre-processing, the memory space of the image is reduced by
scaling the gray-level of the pixels in the range 0-255.
23. SEGMENTATION
• Brain tumour segmentation involves the process of separating the tumour tissues
(Region of Interest – ROI) from normal brain tissues and solid brain tumour with the
help of MRI images or other imaging modalities.
• Its mechanism is based on identifying similar type of subjects inside an image and
forms a group of such by either finding the similarity measure between the objects
and group the objects having most similarity or finding the dissimilarity measure
among the objects and separate the most dissimilar objects in the space
• Segmentation can be done by using Neural Networks
24. FUTURE EXTRACTION
Feature Extraction is the mathematical statistical procedure that extracts the
quantitative parameter of resolution changes/abnormalities that are not visible to the
naked eye.
Feature Extraction is identifying abnormalities. We need to extract some features
from images as we need to do classification of the images using a classifier which
needs these features to get trained on.
In feature Extraction we have used 32 filters twice in convolution layer at two
different times. These makes 32*32 filters applied with combinations to extract
features from the image which makes the model well trained to get the classified
output accurately.
25. • CNN is used for feature extraction. An image after pre-processing, of size
150X150X3 is fed as an input to the neural network.
• A Convolutional Layer is added with 32 filters, a kernel size of 3X3 and a stride of
[1,1] using Conv2D class.
• Rectified Lineal Unit (ReLU) activation function is added to bring non-linearity to
the model. Max Pooling is done to the output obtained from the previous layer which
serve as an input to this layer.
• The above steps are repeated again starting from Convolutional layer till Max
Pooling so that the model is very well trained and we get an accurate Results.
• All this steps are executed in sequential order. Later, Flatten Layer is added along
with ReLU with 25% dropout to get a Fully Connected Layer.
26. CLASSIFICATION
In our proposed framework, we adopt the concept of transfer learning and uses
several pre-trained deep convolutional neural networks to extract deep features from
brain magnetic resonance (MR) images.
The extracted deep features are then evaluated by several machine learning
classifiers.
The top three deep features which perform well on several machine learning
classifiers are selected and concatenated as an ensemble of deep features which is
then fed into several machine learning classifiers to predict the final output.
After the feature Extraction step, we will get the fully connected layers with 32
hidden neurons in fully connected (FC) layer. Again after FC layer is added along
with ReLU with 25% dropout to get a Fully Connected Layer with 4 hidden neurons
in it.
A Softmax Activation Function is added, so that we get the classified output. We get
prediction of the type of tumor along with the accuracy.
27. TO SUMMERIZE
• Data set collection.
• Training and testing the images.
• Brain Tumor images are taken as input image.
• For Blurriness removal purpose, the input image converted into gray scale.
• Extracting the features from the images
• Classifying the types of Tumors.
28. CNN
• In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of
deep neural network, most commonly applied to analyse visual imagery.
• CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons
usually mean fully connected networks, that is, each neuron in one layer is connected
to all neurons in the next layer.
• The "full connectivity" of these networks makes them prone to overfitting data.
• CNNs use relatively little pre-processing compared to other image classification
algorithms.
• This means that the network learns to optimize the filters (or kernels) through
automated learning, whereas in traditional algorithms these filters are hand-
engineered.
• This independence from prior knowledge and human intervention in feature
extraction is a major advantage. Convolutional layers are the major building blocks
used in convolutional neural networks.
29. • CNN is a class of deep neural networks that uses the convolutional layers for
filtering inputs for useful information.
• The convolutional layers of CNN apply the convolutional filters to the input for
computing the output of neurons that are connected to local regions in the input.
• It helps in extracting the spatial and temporal features in an image. A weight sharing
method is used in the convolutional layers of CNN to reduce the total number of
parameters
• CNN is generally comprised of three building blocks:
(1) a convolutional layer to learn the spatial and temporal features,
(2) a subsampling (max-pooling) layer to reduce or down sample the dimensionality of
an input image, and
(3) a fully connected (FC) layer for classifying the input image into various classes.
31. Table: New CNN architecture. All network layers are listed with their properties.
Layer No. Layer Name Layer Properties
1. Image Input 150 X 150 X 3 images
2. Convolutional 32 3 X3 convolutions with stride [1 1]
3. Rectified Linear Unit Rectified Linear Unit
4. Max Pooling 2 X 2 max pooling
5. Convolutional 32 3 X3 convolutions with stride [1 1]
6. Rectified Linear Unit Rectified Linear Unit
7. Max Pooling 2 X 2 max pooling
8. Flatten Converting to single dimension
9. Fully Connected 32 hidden neurons in fully connected (FC) layer
10. Rectified Linear Unit Rectified Linear Unit
11. Dropout 25% dropout
12. Fully Connected 4 hidden neurons in fully connected (FC) layer
13. Softmax Softmax
14. Classification Output 4 output classes, “0” for a meningioma tumor, “1” for a glioma tumor, and
“3” for a pituitary tumor, “4” for no tumor
33. CONVOLUTIONAL LAYER
• A CNN is a neural network with some convolutional layers (and some other layers).
A convolutional layer has a number of filters that does convolutional operation.
34. 6 x 6 image
These are the network parameters to be learned.
Filter 1
Each filter detects a small pattern (3 x 3).
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