Deep Learning-Based Classification of Blood Groups, Cell Counts, Ages, and Genders
Objective
This study uses deep learning to classify biomedical data, including blood groups, cell counts, ages, and genders. It employs a Convolutional Neural Network (CNN) with pre-processing techniques like image resizing and noise removal.
Abstract
This study proposes a comprehensive approach for multi-class classification of diverse biomedical parameters, encompassing blood groups, cell counts, ages, and genders, employing deep learning techniques. The dataset preparation involves pre-processing steps such as image resizing and noise removal. For blood group classification, a smear blood dataset is utilized, where the task involves distinguishing between blood groups A, B, AB, and O. The cell count classification is performed on a blood nucleus dataset containing digit representations. Age and gender classification is conducted on a fingerprint dataset, where age is considered as a range and gender as binary (Male/Female). The deep learning model employed for these tasks is a Convolutional Neural Network (CNN). The convolutional layers of the CNN facilitate hierarchical feature learning, enabling the model to discern intricate patterns and relationships within the input data. By leveraging this methodology, the study aims to enhance the accuracy and efficiency of biomedical parameter classification, contributing to the broader field of medical diagnostics and analysis.
Keywords: Datasets, Preprocessing, Deep Learning, Convolution Neural Network, Classification, 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
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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.
