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AI-Based Medical Image Analysis - 15 Days

AI-Based Medical Image Analysis - 15 Days ₹149. Build diagnostic AI for medical images.

Duration: 15 Days
Location: Offline / Online (Anywhere in India)
Code: AI-MED-101
₹249
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Course Overview

Program Description

15-day intensive course on applying Artificial Intelligence and Deep Learning to medical image analysis. Learn to develop AI models for detecting, classifying, and segmenting medical images including X-rays, CT scans, MRI, and ultrasound. Build diagnostic AI systems from scratch.

Skills You'll Learn

Medical Image Analysis
Deep Learning
CNNs
Transfer Learning
U-Net Segmentation
TensorFlow
PyTorch
OpenCV
DICOM
Grad-CAM
XAI
Flask
FastAPI
DICOM Processing
Brain Tumor Segmentation
Pneumonia Detection
Diabetic Retinopathy

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

1

D1: Introduction to Medical Image Analysis

Overview, applications, DICOM, PACS, clinical workflows. Setting up Python environment (Python, Jupyter, TensorFlow, PyTorch).

2

D2: Medical Imaging Modalities

X-ray, CT scan, MRI, Ultrasound, PET, SPECT, modalities comparison. Visualize DICOM images with PyDICOM and ITK.

3

D3: Image Preprocessing

Noise reduction, intensity normalization, histogram equalization, registration. Preprocess chest X-ray dataset using OpenCV and scikit-image.

4

D4: Feature Extraction & Augmentation

ROI, texture analysis, HOG, data augmentation, synthetic data generation. Implement data augmentation with Albumentations.

5

D5: Fundamentals of Medical Image Segmentation

Thresholding, edge detection, watershed, active contours. Segment lung fields in X-ray images using OpenCV.

6

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.

7

D7: Convolutional Neural Networks (CNNs)

CNN layers (convolution, pooling, flatten), filters, feature maps. Build CNN for disease classification from scratch in TensorFlow/Keras.

8

D8: Transfer Learning in Medical Imaging

Pretrained models (ResNet50, EfficientNet, VGG16, DenseNet121), fine-tuning. Fine-tune ResNet50 or EfficientNet for chest X-ray classification.

9

D9: Advanced Architectures

U-Net for segmentation, YOLO, Mask R-CNN for object detection. Implement U-Net for lung segmentation (with Kaggle DSB).

10

D10: Medical Image Segmentation (Advanced)

U-Net architecture, encoder-decoder networks, multi-class segmentation. Implement 3D U-Net for brain tumor segmentation (BraTS dataset).

11

D11: Explainable AI (XAI) in Healthcare

Grad-CAM, LIME, SHAP, feature visualization. Apply Grad-CAM to CNN model for interpretability.

12

D12: Deployment & Integration

Flask/FastAPI API creation, cloud (AWS/GCP) integration, mobile edge deployment. Deploy model as REST API for image-based diagnosis.

13

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)).

14

D14: Project Development

Build complete AI-based medical image analysis system. Full system integration and testing.

15

D15: Project Presentation & Certification

Final project demo, viva voce, course wrap-up. Present prototype and receive certificate.

Key Features

3 Live Projects: Pneumonia Detection + Brain Tumor Segmentation + Diabetic Retinopathy
Complete Source Code Access
Cloud GPU Access (Online) / Lab Access (Offline)
ISO 9001:2015 Certified Certification
3 Real Projects
Project Report & Documentation
Video Tutorials
GitHub Portfolio
Explainable AI Implementation

What You'll Learn

Process medical images (DICOM, NIfTI) for AI analysis
Build CNN models for disease detection
Segment tumors and organs using U-Net
Classify medical images using transfer learning
Deploy AI models using Flask/FastAPI
Apply explainable AI techniques
Ensure medical AI safety and compliance

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
AI-Based Medical Image Analysis - 15 Days

Internship Details

Duration: 15 Days
Company: NextVision Infotech
Industry: Engineering & Technology
Location: Offline / Online (Anywhere in India)
Available Seats: 30
Course Code: AI-MED-101

Prerequisites

  • Basic Python programming knowledge
  • Understanding of machine learning fundamentals
  • Basic knowledge of biology or medicine
  • Biomedical or Engineering background preferred

Target Audience

  • 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

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