BLOOD GROUP DETECTION USING IMAGE PROCESSING & MACHINE LEARNING
Objective
This paper introduces a non-invasive method for blood group determination. It combines image processing and machine learning to classify blood samples by analysing red blood cells mixed with specific antibodies.
Abstract
Blood group determination is crucial in various medical and clinical scenarios, such as blood transfusions, organ transplants, and genetic studies. This paper presents an innovative approach for non-invasive blood group detection by leveraging the synergy of image processing and machine learning techniques. Our proposed system begins by capturing a digital image of a blood sample slide, which contains red blood cells (RBCs) mixed with known antibodies. The acquired image undergoes a preprocessing phase, where noise is removed, and RBCs are segmented. Subsequently, feature extraction techniques are applied to quantify the presence of different blood group antigens on the RBCs. The core of our system relies on a machine learning model, trained on a comprehensive dataset of blood samples with known blood group types. This model classifies the blood sample into one of the major blood group categories (A, B, AB, or O) and the Rh factor (positive or negative).
Keywords: SVM, features Extraction, Blood Group detection, blood cell.
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Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
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