Combining Neural Network Models for Blood Cell Classification

2021/01/10 by Indraneel Ghosh, Ghosh, Indraneel, Siddhant Kundu +1
Computer Science · Health Professions · #AI in cancer detection #Artificial Intelligence in Healthcare #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2101.03604

openalex publication_date 2021/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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