CBC Analysis & Anemia Detection Project

An Advanced Medical Analysis Platform for Healthcare Professionals and Researchers

Project Overview

This project combines advanced machine learning techniques with medical imaging to provide an innovative approach to anemia detection and CBC analysis. The system utilizes two main components:

  • AI-powered conjunctiva image analysis for anemia detection
  • Comprehensive CBC results analysis and interpretation

Technical Details

Technology Stack

Backend
  • Python 3.x
  • Flask Framework
  • TensorFlow/Keras
  • OpenCV
Frontend
  • HTML5/CSS3
  • JavaScript
  • Bootstrap 5
  • Chart.js

Technical Documentation

Jupyter Notebook Documentation

View our detailed technical analysis and implementation details in this interactive Jupyter notebook.

View Notebook View in Kaggle

Methodology

Data Collection and Analysis

Our methodology involves comprehensive analysis of:

  • Complete Blood Count (CBC) parameters
  • Conjunctiva images for non-invasive analysis
  • Clinical correlations and patterns

Machine Learning Pipeline

The AI model utilizes:

  • Deep Convolutional Neural Networks (CNN)
  • Transfer Learning techniques
  • Advanced image preprocessing

Research Findings

Novel Blood Pattern Analysis

Our research introduces a groundbreaking approach to CBC analysis, identifying distinct patterns that revolutionize how we understand and interpret blood disorders.

Pattern Classification System
Pattern Notation: Values are represented as [HGB_RBC_HCT]
Example: "Normal_Low_Normal" indicates Normal HGB, Low RBC, and Normal HCT levels
Normal State Pattern
Normal_Normal_Normal
  • Predominant normocytic population
  • Minimal macrocytic and microcytic presence
  • Normal HGB distribution
Early Warning Pattern
Normal_High_Normal
  • Balanced normocytic and microcytic distribution
  • No macrocytic presence
  • Indicates compensatory mechanism activation
Critical Pattern
Low_High Patterns
  • Thalassemia Variant: MCV < 65 fL
  • High RDW with elevated RBC
  • Requires immediate clinical attention
Progressive Pattern
Low_Normal Patterns
  • Shows clear disease progression
  • Progressive RDW elevation
  • Critical monitoring points needed
Pattern Progression Models
Microcytic Progression
Normal
Normal_High_Normal
Low_Normal
Low_High
Macrocytic Progression
Normal
Normal_Low_Normal
Low_Low_Normal
Low_Low_Low
Clinical Applications
Pattern Identification
  • Initial classification
  • Trend analysis
  • Risk assessment
Monitoring
  • Regular assessment
  • Parameter tracking
  • Early intervention
AI Integration
  • Pattern recognition
  • Predictive modeling
  • Decision support

Implementation

System Architecture

The application implements a modular architecture with:

  • Scalable microservices design
  • Secure API endpoints
  • Efficient data processing pipeline

Resources

Additional Resources