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Development of AI-Powered Microbiological Analysis Library for Automated Colony Counting and Classification
  1. case
  2. Development of AI-Powered Microbiological Analysis Library for Automated Colony Counting and Classification

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Development of AI-Powered Microbiological Analysis Library for Automated Colony Counting and Classification

neurosys.com
Medical
Food & Beverage
Cosmetics
Veterinary

Challenges in Manual Microbiological Analysis

Manual counting and classification of bacterial colonies on Petri dishes is time-consuming, error-prone, and requires specialized expertise. Current methods lack scalability and integration with modern laboratory automation systems, leading to inefficiencies in healthcare, pharmaceutical, and food safety sectors.

About the Client

Technology company specializing in AI-driven solutions for laboratory automation and microbiological analysis

Key Project Goals

  • Develop a flexible AI library for automated microorganism identification and classification
  • Achieve high-accuracy colony counting using RGB and 3D image data
  • Enable seamless integration with existing lab automation systems
  • Support customization for new microorganism types and imaging conditions
  • Reduce manual labor requirements in microbiological analysis

Core System Capabilities

  • Automated colony detection and counting from RGB/3D images
  • Multi-species classification using deep learning models
  • Image processing pipeline for noise reduction and enhancement
  • API integration with laboratory automation systems
  • Cross-platform standalone application with GUI

Technology Stack

TensorFlow
OpenCV
Point Cloud Library
PostgreSQL
Flask

System Integrations

  • MicroTechniX lab automation systems
  • Zeiss imaging hardware APIs
  • Laboratory Information Management Systems (LIMS)

Performance Requirements

  • 99.9% system availability for critical operations
  • Processing latency under 5 seconds per image set
  • Support for 1000+ concurrent analysis requests
  • Data encryption for sensitive microbiological data
  • Cross-lab consistency under varying imaging conditions

Expected Business Impact

Enables 80% faster microbiological analysis with 95%+ accuracy, reduces manual labor costs by 70%, and creates new revenue streams through SaaS licensing. The solution will establish NeuroSYS as a leader in AI-driven laboratory automation across healthcare, pharmaceutical, and food safety industries.

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