2. DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
MAHARAJAAGRASEN INSTITUTE OF TECHNOLOGY
(AFFILIATED TO GURU GOBIND SINGH INDRAPRASTHA UNIVERSITY, DELHI)
Guide Name: Mr. Anupam Kumar
Theme of Project: Machine Learning
Project ID:- CSE2-73
Project Team Members:-
Vishal Tiwari
35196407220
Deepak Kumar Verma
02196402719
Vipul
04196402719
3. PROBLEM STATEMENT
SELECTION OF RELEVANT FEATURES FROM MEDICAL IMAGES USING
HYBRIDIZATION OF THE WHALE OPTIMIZATION ALGORITHM (WOA) AND
DRAGONFLY ALGORITHM (DA) & CLASSIFICATION OF THE EXTRACTED
FEATURES
4. TECHNOLOGY STACK
1. PYTHON
2. JUPYTER NOTEBOOK
3. GOOGLE COLLAB
4. PYTHON LIBRARIES: -
TENSORFLOW
PYTORCH
KERAS
SKLEARN
MATPLOTLIB
5. INTRODUCTION
BIO-INSPIRED COMPUTING REPRESENTS THE UMBRELLA OF DIFFERENT STUDIES OF
COMPUTER SCIENCE, MATHEMATICS, AND BIOLOGY IN THE LAST YEARS. BIO-INSPIRED
COMPUTING OPTIMIZATION ALGORITHMS IS AN EMERGING APPROACH WHICH IS BASED ON
THE PRINCIPLES AND INSPIRATION OF THE BIOLOGICAL EVOLUTION OF NATURE TO DEVELOP
NEW AND ROBUST COMPETING TECHNIQUES.
USING HYBRID BIO INSPIRED ALGORITHM THAT USES MEDICAL IMAGES DATASET FOR
ANALYZING AND MEASURE ACCURACY. IN THIS PROJECT, THE FEATURES FROM THE SCANNED
DATASETS ARE EXTRACTED AND THE RELEVANT FEATURES ARE THEN SELECTED. TWO-HYBRID
OPTIMIZATION APPROACHES ARE PROPOSED. THESE APPROACHES IMPLEMENT WHALE
OPTIMIZATION ALGORITHM (WOA) AND DRAGONFLY ALGORITHM (DA) COMBINED WITH AN
SVM CLASSIFIER (WOA-SVM AND DA-SVM) TO OPTIMIZE ITS PARAMETERS AND OBTAIN THE
OPTIMAL CLASSIFICATION ACCURACY.
6. SCOPE & MOTIVATION
THE PREVIOUSLY PROPOSED DEEP LEARNING (DL) MODELS REQUIRE EXTENSIVE AMOUNTS
OF DATA IN ORDER TO BE TRAINED, WHICH COULD BE DIFFICULT TO OBTAIN, IN CASE OF
PANDEMIC SUCH AS COVID-19. HENCE, WE REQUIRE A ROBUST SOLUTION THAT CAN WORK
ON SMALL DATASET AND HAS COMPARABLE OR HIGHER ACCURACY THAN STATE-OF-THE-
ART DL MODELS.
CURRENTLY, THE BIO-INSPIRED OPTIMIZATION ALGORITHM COULD HYBRIDIZE TOGETHER.
DUE TO THE PROBLEM OF CONVERGENCE SPEED WHICH CAN BE ENCOUNTERED IN SOLVING
REAL CHALLENGING APPLICATIONS AND IN THE FUTURE THESE BIO-INSPIRED ALGORITHMS
COULD BE HYBRIDIZED WITH OTHER APPROACHES AND METHODS SUCH AS QUANTUM
COMPUTING AND CHAOTIC THEORY TO ENHANCE THE PERFORMANCE OF BIO-INSPIRED
OPTIMIZATION ALGORITHMS.
7. LITERATURE SURVEY
WHALE OPTIMIZATION ALGORITHM (WOA) IS A META-HEURISTIC
ALGORITHM. IT IS A
NEW ALGORITHM, IT SIMULATES THE BEHAVIOR OF HUMPBACK WHALES
IN THEIR SEARCH
FOR FOOD AND MIGRATION.
WHALE OPTIMIZATION ALGORITHM FEATURES:-
1. ALGORITHMS ARE EASY TO IMPLEMENT.
2. THIS ALGORITHM IS HIGHLY FLEXIBLE.
3. DO NOT NEED MANY PARAMETERS.
4. YOU CAN EASILY NAVIGATE THROUGH EXPLORATION AND
EXPLOITATION BASED ON ONE
PARAMETER.
5. DUE TO THE SIMPLICITY OF THIS ALGORITHM AND ITS LACK OF MANY
PARAMETERS, IT IS
USED TO SOLVE THE LOGARITHMIC SPIRAL FUNCTION, IT COVERS THE
BOUNDARY AREA IN THE
RESEARCH SPACE.
6. THE POSITION OF THE ELEMENTS (SOLUTIONS) IN THE EXPLORATION
PHASE IS IMPROVED BASED ON RANDOMLY SELECTED SOLUTIONS
RATHER THAN THE BEST SOLUTION OBTAINED SO FAR .
8. LITERATURE SURVEY
DRAGONFLY ALGORITHM (DA) ALGORITHM ORIGINATES
FROM STATIC AND DYNAMIC SWARMING BEHAVIOURS. THESE
TWO SWARMING BEHAVIOURS ARE VERY SIMILAR TO THE
TWO MAIN PHASES OF OPTIMIZATION USING META-
HEURISTICS: EXPLORATION AND EXPLOITATION.
DRAGONFLIES CREATE SUB SWARMS AND FLY OVER
DIFFERENT AREAS IN A STATIC SWARM, WHICH IS THE MAIN
OBJECTIVE OF THE EXPLORATION PHASE. IN THE STATIC
SWARM, HOWEVER, DRAGONFLIES FLY IN BIGGER SWARMS
AND ALONG ONE DIRECTION, WHICH IS FAVOURABLE
9. APPROACH
For Image Dataset we will be using Kaggle. After that We will resize it.
Two-hybrid optimization image classification approaches are proposed. These approaches implement WOA and DA
combined with an SVM classifier (WOA-SVM and DA-SVM) to optimize its parameters and obtain the optimal
classification accuracy.
Then, the dataset is trained using the optimal parameters of SVM to get the learning model. This model is used to
predict the test data and gain optimal classification accuracy.
After getting the optimal parameters, We will train the model.
After Obtaining the model we will use the model to classify and measure accuracy between 4 test cases of Breast
cancer, Lung cancer, Brain tumor & Healthy image.
11. References
1. M. Abdel-Zaher and A. M. Eldeib, ‘‘Breast cancer classification using deep belief networks,’’ Expert Syst. Appl., vol.
46, pp. 139–144, Mar. 2016, doi: 10.1016/j.eswa.2015.10.015.
2. M. Dorigo, “Optimization, learning and natural algorithms,” Dipartimento di Elettronica Politecnico di Milano,
Milan, Italy, 1992, Ph.D. thesis.
3. S. Mirjalili, ‘‘Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective,
discrete, and multi-objective problems,’’ Neural Comput. Appl., vol. 27, no. 4, pp. 1053–1073, May 2016, doi:
10.1007/s00521-015-1920-1.
4. A. Bhardwaj and A. Tiwari, ‘‘Breast cancer diagnosis using genetically optimized neural network model,’’ Expert
Syst. Appl., vol. 42, no. 10, pp. 4611–4620, 2015, doi: 10.1016/j.eswa.2015.0
5. T. Hu, M. Khishe, M. Mohammadi, G.-R. Parvizi, S. H. Taher Karim, and T. A. Rashid, ‘‘Real-time COVID-19 diagnosis
from X-ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm,’’
Biomed. Signal Process. Control, vol. 68, Jul. 2021, Art. no. 102764, doi: 10.1016/j.bspc.2021.102764.
