Classification of Heart Diseases Based on Cardiovascular Signals using Deep Learning
Objective
Develop an integrated CNN and ResNet-based system enhanced by Osprey Optimization Algorithm for accurate heart disease classification.
Abstract
Heart is the most vital organ of a human body. So, it is necessary to classify the condition as accurate as possible. In this paper, we will use an integrated system of CNN and ResNet based neural network for classifying the heart diseases which is more efficient in producing accurate results. And to improve the accuracy even further we will introduce Osprey Optimization Algorithm (OOA) strategy. The network will be trained on the features like heart rates and RR intervals of ECG signals and the diseases like Arrhythmia, Heart Stroke, Heart Muscle Disease, Coronary Artery Disease, Aorta Disease and Heart Valve Disease and the stages of particular heart disease will also be classified at the end except for Heart Muscle Disease which don’t have such kind of classification. Then using the same values we will test the network also. And finally we will use the OOA optimization to improve the accuracy to even more. And the classifier’s performance will be validated on the basis of parameters like accuracy, specificity, sensitivity, MSE, PSNR, TP, TN, FP and FN etc.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
