Update README.md

README.md

@@ -3,11 +3,89 @@ license: cc-by-sa-4.0
 dataset_info:
   features:
   - name: image
-    dtype: 'null'
+    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.