project engineering

1. SRI KRISHNA INSTITUTE OF TECHNOLOGY
DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING
A
FINAL PHASE PROJECT PRESENTATION
ON
“ ANALYSIS AND DESIGN OF HYBRID FEATURE EXTRACTION METHOD WITH
RELM FOR BRAIN TUMOR CLASSIFICATION ”
PRESENTED BY
BHAVANI S [1KT16IS002]
CHITRASHREE K [1KT16IS007]
GUNASHREE S GOWDA [1KT16IS011]
PRABHA V [1KT16IS034]
UNDER THE GUIDANCE OF
Dr. HEMALATHA K.L.,
PROFESSOR AND HOD,
DEPT. OF ISE
1 Dept of ISE, SKIT 18-08-2020

2.  Abstract
 System Analysis.
 System Design.
 Implementation
 References
2 Dept of ISE, SKIT 18-08-2020
 Introduction
 Literature Survey
 Problem Definition
 Testing
 Conclusion & Future Enhancement
 Results
 Snapshots

3. ABSTRACT
 The proposed system uses the Hybrid Feature Extraction
Method.
 Principle Component Analysis (PCA) is used to extract
features.
 Regularized Extreme Learning Machine (RELM) is used for
Classifying Brain Tumor.
3 Dept of ISE, SKIT 18-08-2020

4. INTRODUCTION
 Brain tumours are abnormal growth of cells in the brain.
 MRI, CT-scan, X-ray are some examples of Medical
Imaging Analysis.
 Brain tumours are diagnosed through MRI brain images.
 Many automated systems are developed for detection of
Brain tumours.
4 Dept of ISE, SKIT 18-08-2020

5. Contd…
 Image Processing is a method to perform some operations
on images.
 Regularized Extreme Learning Machine is a model used
for classification and regression method.
 Artificial Neural Network is a neural network that uses
perceptron.
 Grid Search Algorithm is the process of scanning the data
to configure optimal parameters for a given model.
 MRI Images are produced by Radiology Technique.
5 Dept of ISE,SKIT 18-08-2020

6. Authors Title Objectives Algorithm Concepts Journal/
Conferen
ce/Year
Demeri
ts
Vishnu Kumar,
Syed and
Suthar
 Mammogram
of Breast
Cancer
Detection
using Image
Enhancement .
To enhance the
image contrast
and classify
mammogram of
breast cancer.
1. Gabor Filter
algorithm.
2. Marker-
Controlled
Watershed
algorithm.
1.Noise
elimination.
2.Edge
enhancement.
International
journal of
engineering
technology
and
advanced
engineering
-2012.
1.Classific
ation rates
are less.
2. Failed
to validate
Standard
datasets.
3.Comput
ational
time
more.
Dina, Sammy
and Selim
 Detection of
Brain Tumour
using Image
Processing and
Probabilistic
Neural
Network
Techniques.
To enhance
automated brain
tumour detection
and identification
using image
processing and
probabilistic
neural network
techniques.
1. Learning
vector
Quantization.
2. Probabilistic
Neural
Network
1.To modified
image
segmentation
techniques were
applied on MRI
scan images.
International
Journal of
Images
Processing
and visual
communicat
ion -2012.
1.Needs to
improve
Network
Structure
Determina
tion.
COMPARITIVE SUMMARY OF LITERATUTRE
SURVEY
6 Dept of ISE, SKIT 18-08-2020

7. Authors Title Objectives Algorithm Concepts Journal/Co
nference/Y
ear
Demerits
Anant
Bharadwaj,
Kapil Kumar
Siddu
 An approach
to Medical
Image
classification
using neuro
FUZZY
logic and
ANFIS
classifier.
To proposed
strategy to
medical image
classification
of patient’s
MRI scan
images of the
brain.
1. ANFIS
Algorithm
2. PCA
algorithm
1. Extract
features and
applied ANFIS
tool for
training.
International
Journal of
computer
Trends and
technology -
2013.
1.Accuracy
rates are less.
2.Less clarity
on dataset.
Karthik,
Menaka and
Chellamuthu
 Method of
Detecting
Brain
Tumours
from MRI
images.
To enhance the
method of
detecting brain
tumours from
MRI images.
1. Grey Level
Co-occurrence
Matrix.
2. Watershed
Transform.
1.GLCM to
extract the
features.
2. SVM for
analysis of
classification.
International
journal of
biomedical
engineering
and
technology-
2015.
1.Classification
of types of
brain tumor
was not
possible.
Contd…
7 Dept of ISE, SKIT 18-08-2020

8. PROBLEM DEFINITION
 To develop a Analysis and Design of Hybrid Feature Extraction
Method with RELM for Brain Tumor Classification .
8 Dept of ISE,SKIT 18-08-2020

9. SYSTEM ANALYSIS
9 Dept of ISE, SKIT 18-08-2020
DRAWBACKS OF EXISITING SYSTEM
 Computational time is more.
 Needs to improve Network Structure.
 Accuracy rates are less.
 Classification of types of brain tumor was not possible.

10. Contd…
10 Dept of ISE, SKIT 18-08-2020
OBJECTIVES OF PROPOSED SYSTEM
 To achieve higher accuracy rates.
 To achieve higher classification rate with less number of training data.
 To classify the type of brain tumour.
 To reduce computational time.

11. SYSTEM DESIGN
11 Dept of ISE, SKIT 18-08-2020
Figure 1: Architecture of Automated Brain Tumor Detection System using RELM

12. DATA FLOW DAIGRAM
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Figure 2: Data Flow Diagram Level-0
DFD LEVEL-0

13. DATA FLOW DAIGRAM
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Figure 2: Data Flow Diagram Level-1
DFD LEVEL-1
Figure 3: Data Flow Diagram Level-1

14. DATA FLOW DAIGRAM
14 Dept of ISE, SKIT 18-08-2020
Figure 4: Data Flow Diagram Level-2
DFD LEVEL-2

15. IMPLEMENTATION
15 Dept of ISE, SKIT 18-08-2020
 Pseudocode for Training the RELM algorithm module.
 Pseudocode for Testing Module.
 Pseudocode for Automated Brain Tumor Classification
Module.

16. Contd…
16 Dept of ISE, SKIT 18-08-2020
Pseudo Code : Training Module
Input: Brain MRI image.
Output: features are extracted and model is trained.
Training the RELM algorithm module:
Step 1: The RELM algorithm recognizes the brain MRI images.
Step 2: The features from the recognized images are taken as input.
Step 3: The RELM algorithm is applied.

17. Contd…
17 Dept of ISE, SKIT 18-08-2020
Input: The testing image is added as input.
Output: The MRI image is classified as tumorous or non tumorous.
Step 1: Select the MRI image using “Get Image”.
Step 2: Select “Analayze Image” to know about the status of MRI image.
Step 3: If the MRI image is tumorous:
“Know more” text field appears
else
The MRI image is classified as non tumorous.
Step 4: Click on “Know more” text field to get the knowledge of the tumor.
Pseudo Code : Testing Module

18. Contd…
18 Dept of ISE, SKIT 18-08-2020
Pseudo Code : Automated Brain Tumor Classification
Module
Input: Brain MRI features are extracted.
Output: Detection and Classification of tumor.
Step1: Input the testing MRI image.
Step 2: Extraction of the features from brain MRI images.
Step 3: If tumor is detected:
Classify if tumor type is HGG:
Print HGG tumor.
Else if tumor type is LGG:
Print LGG tumor.

19. 19 Dept of ISE,SKIT 18-08-2020
UNIT TESTING
SL NO Input Expected Output Actual Output Satisfactory/Unsatis
factory
1 Unit Testing for
Valid Image of size
96 × 96
Analyse successful. Analyse
successful
Satisfactory
2 Unit Testing for Non
tumorous image or
any unknown image
of size 96 × 96
Analysis failed due
to improper data in
image.
Analysis
successful, shows
Non-Tumorous.
Unsatisfactory
3 Unit Testing for
Tumorous image of
size 96 × 96
Status of the Image
is Tumorous.
Status of the
Image is
Tumorous
Satisfactory
4 Unit Testing for
Invalid
Format(.doc,.docx,
pdf)
Invalid Format Invalid formats
not shown during
selection.
Unsatisfactory

20. 20 Dept of ISE,SKIT 18-08-2020
INTEGRATION TESTING
SL NO Input Expected Output Actual Output Satisfactory/Unsatisfa
ctory
1 Integration Testing
for click on the
“know more” of
tumorous image
Type of Tumor with
its Description
Type of Tumor
with its
Description
Satisfactory
2 Integration Testing
for Non-Tumorous
Image of size
96*96
“Know more” button
displayed along with
status
“Know more”
button
disappears,
status shown
Unsatisfactory

21. 21 Dept of ISE,SKIT 18-08-2020
SYSTEM TESTING
SL NO Input Expected Output Actual Output Satisfactory/Unsatisfa
ctory
1 System Testing for
GUI (dialogue) box
opened when
application is
running.
Alignment of
buttons and text is
proper. Usability of
application is
easier.
Alignment of
buttons and text
is proper.
Usability of
application is
easier.
Satisfactory
2 Running the
application with
4GB ram system by
giving input as a
tumorous image of
size 96 × 96.
Less Response time
taken to give the
results.
More response
time taken to
obtain results.
Unsatisfactory

22. 22 Dept of ISE,SKIT 18-08-2020
 The dataset used for the proposed approach in total is 310 images. Out of which 112 are
of type HGG ,99 are of type LGG and 99 are the normal MRI brain images.
 The dataset was further divided randomly. Out of the total 310 MRI brain images 15 were
randomly chosen for testing.
 The remaining 295 images were categorized and kept for training purpose.
 The features are fed into RELM training module and finally the model named
“Pokedex” is trained.
 Random MRI images are chosen for the testing purpose and the classification is
performed on them to know whether they are non-tumorous or tumorous.
 If they are tumorous they would be further classified and identifies the type of tumor, here
it is High grade Gliomas (HGG) or Low Grade Gliomas (LGG).

23. 23 Dept of ISE,SKIT 18-08-2020
Confidence Matrix of one MRI brain image.

24. 24 Dept of ISE,SKIT 18-08-2020
Figure 8.2: Graph of Response time of different class of tumor images given their
processor speed as 8GB

25. 25 Dept of ISE,SKIT 18-08-2020
Figure 8.3: Graph of Response time of different class of tumor images given their
processor speed as 4GB

26. 26 Dept of ISE,SKIT 18-08-2020
The testing image dataset through the “GET
IMAGE” button.
Selection of a Testing Image

27. 27 Dept of ISE,SKIT 18-08-2020
Result of a Non- Tumorous MRI image

28. 28 Dept of ISE,SKIT 18-08-2020
Result of a Tumorous and a LGG type of
Brain tumor
Information about LGG

29. 29 Dept of ISE,SKIT 18-08-2020
Result of a Tumorous and a HGG type of
Brain tumor
Information about HGG

30. CONCLUSION
30 Dept of ISE, SKIT 18-08-2020
 An effective brain tumor classification approach is introduced which has three
main steps.
 At first, brain images are transformed into intensity values using a preprocessing
step.
 Then, the most important features are extracted using a novel and efficient
hybrid method, referred to as PCA-NGIST.
 The present study investigated a technique to classify a given Computed
Tomography image to tumorous or non-tumors.

31. FUTURE ENHANCEMENT
31 Dept of ISE, SKIT 18-08-2020
 Accuracy could further be increased in detecting the types of tumours.
 Image quality can be improved by using hybrid techniques. Image processing
techniques like using Markov models and Anisotropic diffusion.
 The System can be extended to other application.
 The method implemented can be extended for multi-grade tumour
classification of MRI brain images to provide better analysis and treatment
planning.

32. [2] Dina, Samy and Selim, ”Automated Brain tumour Detection using Image
Processing and Probabilistic Network Techniques”, International Journal of
Images Processing and Visual Communication, vol no:1, October 2014.
REFERENCES
[1] Vishnukumar, Syed and Suthar, “Mammogram of Breast Cancer Detection Based
on Image Enhancement Algorithm”, International Journal of Engineering Technology
and Advanced Engineering, Vol no:2, Issue no:8, PP:2250-2459, August 2014
[3] Anant Bharadwaj, Kapil Kumar Siddu, “An approach to Medical Image
Classification Using Neuro Fuzzy Logic and ANFIS Classifier”, International Journal
of Computer Trends and Technology, Vol no:4, Issue no:3, PP-236-240, 2013.
[4] Karthik, Menaka and Chellamuthu, “Method of Detecting brain tumours from MRI
images”, International Journal Of Biomedical Engineering and Technology, PP:168-177,
Febrauary-2015.
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