Combining Neural Network Models for Blood Cell Classification
This work addresses the problem of automating blood cell classification for the pharmaceutical and healthcare industries, potentially aiding in blood test analysis and disease diagnosis.
This study evaluates a combined Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) model for classifying different types of White Blood Cells. The paper aims to demonstrate the efficiency of this hybrid approach for automated blood cell analysis.
The objective of the study is to evaluate the efficiency of a multi layer neural network models built by combining Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN) for solving the problem of classifying different types of White Blood Cells. This can have applications in the pharmaceutical and healthcare industry for automating the analysis of blood tests and other processes requiring identifying the nature of blood cells in a given image sample. It can also be used in the diagnosis of various blood-related diseases in patients.