Paper PublishingMachine Learning Algorithm For Brain Stroke Detection
Objective
The purpose of this paper is to develop an automated early ischemic brain stroke detection system using CNN deep learning algorithm.
Abstract
Machine learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health setting, offering personalized clinical care for stroke patients. ML applications in health care are growing, nonetheless there is a greater need for further investigation in some research fields.
Therefore, this study aimed to systematically review the state of the art on ML techniques for brain stroke and classify the research studies into 2 categories based on their functionalities. By using seven machine learning algorithms we can generate the predictions, they are K-Nearest Neighbors, Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Multi-layer Perceptron (MLP-NN) and Support Vector Machine.
Keywords: CT Scan Image, Machine Learning Algorithms, Stroke Ischemic, Stroke Hemorrhage.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- IDE: PyCharm
- Server side Scripts:HTML,CSS,JS.
- Libraries Used: Pandas, Numpy,Sci-kit Learning.
- Frameworks: Flask.
Learning Outcomes
- Scope of Real Time Application Scenarios.
- How Internet Works.
- What is a search engine and how browser can work.
- What type of technology versions are used.
- Use of HTML , CSS and BOOTSTRAP on UI Designs.
- Data Parsing Front-End to Back-End.
- Working Procedure.
- Introduction to basic technologies used for.
- How project works.
- Input and Output modules.
- Frame work use.
- Datasets properties.
- Machine learning algorithms.
- Smote technique in imbalanced.
- Types of strokes.
- Working of Decision Trees.
- Implementing SVM.
- What is KNN.
- Benefits if ensemble techniques.
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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