Blood Cell Image Classification

Project Code :TCMAPY1081

Objective

The primary objective of this project is to develop a highly accurate and efficient blood cell classification system using a Convolutional Neural Network (CNN) integrated with a Residual Network (ResNet) module. The aim is to leverage deep learning techniques to precisely categorize different types of blood cells, including erythrocytes, leukocytes, and platelets, from microscopic images. By achieving robust classification, the project endeavors to advance automated hematological analysis, enhancing medical diagnostics for various blood-related disorders and diseases.

Abstract

In the realm of medical diagnostics, automated analysis of blood cell images has gained significant attention for its potential to enhance efficiency and accuracy. This study proposes a robust methodology employing Convolutional Neural Networks (CNNs) integrated with a Residual Network (ResNet) module for precise classification of blood cell types. Leveraging the power of deep learning, the proposed CNN-ResNet architecture aims to discern and classify various blood cell types, such as erythrocytes, leukocytes, and platelets, from microscopic images. The utilization of ResNet's residual learning mechanism intends to overcome the challenge of vanishing gradients while enabling the network to delve deeper into feature extraction. Through extensive experimentation and validation on diverse datasets, this approach demonstrates promising results, showcasing superior classification accuracy and robustness. The implications of this work extend to revolutionizing automated hematological analysis, potentially contributing to more efficient and reliable medical diagnostics in the domain of blood-related disorders and diseases.

 KEYWORDS: convolutional neural networks (CNNs), Resnet.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

H/W CONFIGURATION:

Processor - I3/Intel Processor

Hard Disk - 160GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

RAM - 8GB


S/W CONFIGURATION:

Operating System :  Windows 7/8/10

Server side Script :  HTML, CSS, Bootstrap & JS

Programming Language :  Python

Libraries :  Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy

IDE/Workbench :  PyCharm

Technology :  Python 3.6+

Server Deployment :  Xampp Server


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