AI ML Project Title

Brain Stroke Prediction using OpenCV and CNN

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Project Description

What is this project all about

This project develops an automated system to detect brain strokes from MRI scans using deep learning. Convolutional Neural Networks (CNN) are employed for image classification, with OpenCV handling image preprocessing. The solution is built in Python for medical image analysis and early diagnosis support.

Scope: The project focuses on classifying MRI brain images to predict strokes using CNNs and OpenCV. It includes image preprocessing, model training, and evaluation but excludes real-time diagnosis integration or clinical deployment.

Prerequisites

What You Should Know Before Starting

  • Python programming: Control flow, functions, I/O
  • OpenCV basics: Image reading, filtering,
    morphological ops
  • Machine Learning: Supervised learning, CNN
    fundamentals
  • Data Handling: NumPy arrays, pandas Data Frames
  • Neuroanatomy(optional): Brain regions, MRI scan modalities
  • Biomedical Imaging(optional): DICOM, NIfTI formats (for
    extension)

Tools & Technologies

What Tools & Technologies will be used in this project

  • Core Libraries: OpenCV, TensorFlow/Keras, scikit-learn, NumPy
  • Visualization: Matplotlib, Plotly
  • Environment: Jupyter Notebook, Google Colab (GPU-enabled)
  • Deployment: Azure, AWS
  • Datasets: Kaggle Br35H, ATLAS Stroke Dataset, NIH Stroke Dataset

Learning Objectives

What you will learn in this course

  • Understand the fundamentals of medical image analysis using MRI scans.
  • Learn how to preprocess and enhance medical images using OpenCV.
  • Gain hands-on experience in building CNN-based deep learning models for classification tasks.
  • Train, validate, and evaluate models for stroke prediction using real-world datasets.
  • Explore visualization techniques to interpret model performance and predictions.
  • Understand the pipeline for deploying AI solutions in a healthcare context using cloud platforms.

LESSON PLAN

🟦 Module 1: Introduction & Setup (1 Hour)                                   

  • Objective: Set up the development environment and
    understand project goals
  • Topics & Activities:
    • Project walkthrough & clinical importance
    • Introduction to datasets (Br35H, ATLAS)
    • Set up Google Colab with OpenCV & TensorFlow
    • Review MRI modalities and preprocessing needs
    Deliverable: Configured workspace with datasets
    loaded

🟦 Module 2: MRI Preprocessing (2 Hours)                               

  • Objective: Prepare raw MRI scans for analysis using image
    processing.
  • Topics & Activities:
    • OpenCV image pipelines
    • Skull stripping using morphological operations
    • Apply CLAHE for contrast enhancement
    • Gaussian blur & noise filtering
    Deliverable: Preprocessed MRI dataset

🟦 Module 3: Feature Engineering (2 Hours)                               

  •  Objective: Extract meaningful features from MRI for model
    input
  • Topics & Activities:
    • Feature types: Edges (Canny), textures (GLCM),
    histograms
    • Resize, crop, and normalize images
    • Data augmentation (rotation, flips, shifts)
    • Label encoding and data split (train/val/test)
    Deliverable: Feature matrix + augmented training dataset

🟦 Module 3: Feature Engineering (2 Hours)                               

  •  Objective: Extract meaningful features from MRI for model
    input
  • Topics & Activities:
    • Feature types: Edges (Canny), textures (GLCM),
    histograms
    • Resize, crop, and normalize images
    • Data augmentation (rotation, flips, shifts)
    • Label encoding and data split (train/val/test)
    Deliverable: Feature matrix + augmented training dataset

🟦 Module 4: Model Development (3 Hours)                              

  •  Objective: Train a CNN model to classify stroke vs. non
    stroke
  • Topics & Activities:
    • CNN architecture overview
    • Model building using Keras (Conv2D, MaxPooling, Flatten)
    • Compile and train with binary cross-entropy
    • Evaluate performance (accuracy, sensitivity, AUC)
    Deliverable: Trained CNN model with evaluation metrics

🟦 Module 5: Dashboard & Explainability (2 Hours)                              

  • Objective: Build a diagnostic UI and add explainability tools
  • Topics & Activities:
    • Design prediction interface with Streamlit
    • Upload image, run inference, display result
    • Implement Grad-CAM or heatmaps for explainability
    • Overlay attention maps on MRI
    Deliverable: Interactive dashboard with explainable AI

🟦 Module 6: Testing & Deployment (1 Hour)                             

  • Objective: Finalize and deploy the model for clinical
    assistance
  • Topics & Activities:
    • Run end-to-end testing on new MRI samples
    • Save model in HDF5 format
    • Deploy via Streamlit/Flask on local/cloud server
    • Discuss integration into clinical workflows
    Deliverable: Deployable AI tool for stroke prediction

FAQ

This is not just theory. You’ll build a fully working AI-powered IT support automation system — step-by-step, guided by real professionals. It’s practical, project-based, and portfolio-ready.

If you’ve completed some basic programming (especially through our beginner courses), you’re good to go! If not, start with our foundation courses, then return for this project.

Absolutely. You’ll finish with a deployable IT automation project, integrated with GitHub and real-time logging — a great addition to your resume and GitHub portfolio.

It’s beginner-friendly if you’ve completed basic to intermediate programming. If not, take our intro courses first — they’re designed to prepare you for this exact project.

Just install Python, Node.js, and VS Code — we’ll guide you through setup in the first session. Everything else will be covered during the course.

In just 10 focused hours, you’ll learn email automation, GitHub API, WebSocket, and React integration — tools used by real tech teams today.

Yes, you will receive an industry-recognized certificate.

Yes, we specialize in helping businesses find and hire the right talent. Our HR and recruitment services are tailored to meet your company’s specific hiring needs. Contact us to learn how we can support your hiring process.
Please visit our page : WoCons – Custom AI & CAD Solutions | Bengaluru

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