Classification of Human White Blood Cell Images
Objective
The primary objective of this project is to develop an advanced image classification system for accurately categorizing human white blood cell images. Leveraging state-of-the-art machine learning and computer vision techniques, the project aims to create a robust and efficient model capable of distinguishing between different types of white blood cells, such as neutrophils, lymphocytes, monocytes, eosinophils, and basophils.
Abstract
The abstract discusses the significance of white blood cells (WBC) in our immune system's defense against infections and foreign substances. It acknowledges the diverse roles of various white blood cell types and highlights the limitations of the traditional differential test used for classification due to its low efficiency and time-consuming nature.
To address this, the study employs machine learning, specifically a deep learning convolutional neural network (CNN) named "SqueezeNet," to classify white blood cells. The researchers fine-tune the model's hyperparameters, train it, and evaluate its performance using a testing dataset.
This approach presents a modern, potentially more efficient method for classifying white blood cells, offering promise for improved diagnostic accuracy and speed in identifying different types like neutrophils, lymphocytes, monocytes, eosinophils, and basophils.
Keywords: blood cells, convolutional neural networks, SqueezeNet, transfer learning, Deep learning technique, Accuracy.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
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
Paper Publishing
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
