task_categories:image-classification language:en license:cc-by-sa-4.0 size_categories:10K<n<100K region:us medical images TB tuberculosis tb detection models
---
license: cc-by-sa-4.0
dataset_info:
  features:
  - name: image
    dtype: 'jpg'
  - name: filename
    dtype: 'null'
  splits:
  - name: train
  download_size: 0
  dataset_size: 0
task_categories:
- image-classification
language:
- en
tags:
- medical
- images
- TB
- tuberculosis
- tb detection
- models
size_categories:
- 10K<n<100K
---

Project Overview
Tuberculosis (TB) remains a major public health challenge, especially in rural India, which accounts for 26% of the global TB burden. Limited healthcare access, a shortage of medical professionals, and high diagnostic costs exacerbate the issue. This project aims to address the delayed detection of TB in rural India using AI-based chest X-ray analysis, enabling early detection and treatment.

Key Problems
1.	Diagnostic Gaps: Lack of access to quick, accurate TB screening in rural areas.
2.	Resource Constraints: Shortage of trained radiologists.
3.	Inconsistent Imaging Quality: Variable X-ray quality from different machines.
4.	Scalability Challenges: Difficulty scaling traditional screening methods.
5.	Integration Issues: Working within existing healthcare infrastructure.

Solution Approach
Develop an AI-based system for early TB detection using chest X-rays, optimized for mobile devices and designed for use by minimally trained healthcare workers in rural areas.

Key Components:
1.	Deep Learning Model: For detecting TB with high sensitivity and specificity.
2.	Mobile Application: Optimized for use offline on smartphones/tablets.
3.	Scalability: System deployment in rural health centers.
4.	Training Program: For rural healthcare workers to use the system effectively.

Project Goal
1.	Model Development: Create a deep learning model for TB detection with 90% sensitivity and specificity.
2.	Mobile App: Build an offline-capable mobile app for use in rural areas.
3.	Deployment: Implement in 50 rural health centers across 3 states in India.
4.	Time Reduction: Decrease TB diagnosis time by 50% in targeted areas.

Expected Outcomes
1.	Validated AI Model for TB detection optimized for mobile deployment.
2.	Training Program for healthcare workers on the AI system.
3.	Database of anonymized chest X-rays for ongoing research.
4.	Published Research on model development and real-world performance.

Learners' Contributions
Data Collection & Preprocessing
Gather diverse datasets from rural India, implement data augmentation, and ensure data anonymization.

Model Development
Explore deep learning architectures (e.g., CNNs, Vision Transformers) and employ transfer learning techniques.
Model Optimization & Mobile Deployment
Optimize for mobile use with model pruning and quantization techniques for offline deployment.
User Interface Development
Design an intuitive interface for healthcare workers with minimal technical training.
Validation & Testing
Conduct rigorous testing and user acceptance trials with rural healthcare workers.

Impact Assessment
1. Health Impact: 
40-50% increase in early-stage TB detection.
30-35% improvement in treatment success rates.

2. Healthcare System Impact: 
50-60% reduction in time to diagnosis.
70-80% increase in rural healthcare workers' capability to conduct TB screenings.

3. Technological Impact: 
- Increased AI adoption in rural healthcare and better digital health record management.

4. Social Impact: 
- Increased health-seeking behavior and TB awareness in target communities.

5. Beneficiaries: 
- TB patients, families of TB patients, the Indian healthcare system.

Conclusion
This project seeks to bridge the diagnostic gaps in TB detection in rural India by leveraging AI and mobile technology, empowering healthcare workers and improving TB detection and treatment outcomes.