AI-Based Medical Image Analysis - 15 Days
AI-Based Medical Image Analysis - 15 Days ₹199. Build diagnostic AI for medical images.
Course Overview
Program Description
Skills You'll Learn
Projects You'll Work On
Live Project 1: Pneumonia Detection from Chest X-Rays
Build a CNN model using transfer learning to classify chest X-rays as Normal, Viral Pneumonia, or Bacterial Pneumonia. Features: Chest X-Ray Images (Pneumonia) dataset, ResNet50 with fine-tuning, Grad-CAM for explainability, Flask API deployment.
Live Project 2: Brain Tumor Segmentation with U-Net
Build a U-Net model to segment brain tumors from MRI scans. Features: BraTS dataset, 3D U-Net or 2D U-Net with multi-class segmentation, Dice similarity coefficient evaluation, Overlay segmentation masks.
Live Project 3: Diabetic Retinopathy Detection from Fundus Images
Build a deep learning model to detect diabetic retinopathy from retinal fundus images. Features: APTOS 2019 dataset, EfficientNet for multi-class classification, Grading (No DR to Proliferative DR), Web application deployment.
Program Modules
D1: Introduction to Medical Image Analysis
Overview, applications, DICOM, PACS, clinical workflows. Setting up Python environment (Python, Jupyter, TensorFlow, PyTorch).
D2: Medical Imaging Modalities
X-ray, CT scan, MRI, Ultrasound, PET, SPECT, modalities comparison. Visualize DICOM images with PyDICOM and ITK.
D3: Image Preprocessing
Noise reduction, intensity normalization, histogram equalization, registration. Preprocess chest X-ray dataset using OpenCV and scikit-image.
D4: Feature Extraction & Augmentation
ROI, texture analysis, HOG, data augmentation, synthetic data generation. Implement data augmentation with Albumentations.
D5: Fundamentals of Medical Image Segmentation
Thresholding, edge detection, watershed, active contours. Segment lung fields in X-ray images using OpenCV.
D6: Introduction to Deep Learning for Medical Images
CNN architecture overview for medical images, transfer learning. Build a simple CNN for MNIST and medical datasets.
D7: Convolutional Neural Networks (CNNs)
CNN layers (convolution, pooling, flatten), filters, feature maps. Build CNN for disease classification from scratch in TensorFlow/Keras.
D8: Transfer Learning in Medical Imaging
Pretrained models (ResNet50, EfficientNet, VGG16, DenseNet121), fine-tuning. Fine-tune ResNet50 or EfficientNet for chest X-ray classification.
D9: Advanced Architectures
U-Net for segmentation, YOLO, Mask R-CNN for object detection. Implement U-Net for lung segmentation (with Kaggle DSB).
D10: Medical Image Segmentation (Advanced)
U-Net architecture, encoder-decoder networks, multi-class segmentation. Implement 3D U-Net for brain tumor segmentation (BraTS dataset).
D11: Explainable AI (XAI) in Healthcare
Grad-CAM, LIME, SHAP, feature visualization. Apply Grad-CAM to CNN model for interpretability.
D12: Deployment & Integration
Flask/FastAPI API creation, cloud (AWS/GCP) integration, mobile edge deployment. Deploy model as REST API for image-based diagnosis.
D13: Regulatory, Ethical, & Commercial Considerations
FDA approvals, HIPAA/GDPR compliance, bias, medical device software regulations. Draft a regulatory checklist for an AI device (FDA 510(k)).
D14: Project Development
Build complete AI-based medical image analysis system. Full system integration and testing.
D15: Project Presentation & Certification
Final project demo, viva voce, course wrap-up. Present prototype and receive certificate.
Key Features
What You'll Learn
Examination Details
| Exam Name | Full Marks | Pass Marks |
|---|---|---|
| Module-wise Assessment | 100 | 40 |
| Practical Lab Assessment | 100 | 40 |
| Project Evaluation | 100 | 40 |
| Project Viva | 100 | 40 |
Internship Details
Prerequisites
- Basic Python programming knowledge
- Understanding of machine learning fundamentals
- Basic knowledge of biology or medicine
- Biomedical or Engineering background preferred
Who Can Enroll ?
- B.Tech/M.Tech Biomedical Engineering students
- B.Tech/M.Tech Robotics/Automation students
- B.Tech ECE/EEE/CSE students
- Medical imaging researchers
- Healthcare technology professionals
Certificate Preview
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