Accurate automated microbiological analysis

The overall goal of the project was to improve the accuracy of automated microbiological analysis with the help of image recognition using deep learning algorithms. The applied methodology determines whether bacteria appeared on a Petri dish (a positive sample) or it stayed clean (a negative sample) by analyzing sample images without human specialist supervision.

In a nutshell

  • eliminate the necessity of having a human specialist involved in sample analysis (read cost reduction)
  • speed up the process, increase the accuracy, as well as minimize the risk of human error in this process.

Deep learning algorithm for bacterial colonies detection

Our highly accurate deep learning algorithm for bacterial colonies detection was specifically developed for a major customer of our client – one of the world’s top 10 pharma companies.

The initially used conventional computer vision algorithm failed to satisfy the customer’s needs. It produced too many false-positive results while examining images of Petri dishes due to mistakenly recognizing air bubbles appearing on a surface as a bacterial colony. This put the whole project at risk.

For our part we advised the client against the next obvious solution to this problem, namely, improving the image quality by acquiring multispectral cameras. Going that direction would significantly increase project costs, as well as wouldn’t guarantee sufficient resolution (taking into account the quality of those cameras at that point in time on the market). Our team suggested applying deep learning algorithms instead, which helped to cope with the challenge successfully.

Solution for pharma industry and industrial microbiology

Our solution replaced a conventional algorithm that is not reliable enough for production purposes and improved the accuracy significantly even in edge cases (like air bubbles in agar, colonies grown on the rim of the Petri dish etc.)

The image recognition function was created for the narrow purpose: microbiological analysis of a specific type of sample images. However, the deep learning methods used for this project have a broader field of adaptation: medical services, quality control in the pharmaceutical industry and healthcare, industrial microbiology and so on.

Let’s talk and see if we are a match for your next IT project.
Tomasz Kowalczyk CEO NeuroSYS
Tomasz Kowalczyk
CEO at NeuroSYS
Thank you for your application!
Let's get in touch!
We want to get to know you a little bit, but we need some help from your side. Let's start with filling gaps below.
Full name
Please provide us with your full name
Please provide us your current Email
Please provide us with your Phone number
Your LinkedIn profile
Please show us your professional social side :)
Link to your portfolio / GitHub
Please insert your Portfolio / GitHub URL correctly
Nothing to say? Maybe just a little bit? Even "Hi" will work - thanks!
CV file
Please upload your CV
Select file
Please choose one of the following
I hereby authorize the processing of my personal data included in this form for the present recruitment-related purposes by NeuroSYS Sp. z o.o. (Rybacka 7 Street, 53-565 Wrocław) (in accordance with the General Data Protection Regulation (EU) 2016/679 of 27.04.2018 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, as well as repealing Directive 95/46/EC (Data Protection Directive)). I acknowledge that submitting my personal data is voluntary, I have the right to access my data and rectify it.
Read and accept
I hereby authorize the processing of my personal data included in my job application for the needs of future recruitment processes by NeuroSYS Sp. z o.o. (Rybacka 7 Street, 53-565 Wrocław).
Read and accept