Combining Neural Network Models for Blood Cell Classification

• URL: http://arxiv.org/abs/2101.03604v1
• Date: Sun, 10 Jan 2021 18:58:46 GMT
• Title: Combining Neural Network Models for Blood Cell Classification
• Authors: Indraneel Ghosh, Siddhant Kundu
• Abstract summary: 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) 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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