Blood Classification Technique using Deep Learning

Abstract

Abstract: This study aims to develop a system to distinguish blood cells from images. Precise and programmed analysis of blood cell images has been considered as an effective way for the determination of types of blood cells such as Eosinophil, Lymphocyte, Monocyte, Neutrophil. In this work, we extracted different blood cell features such as Eosinophil, Lymphocyte, Monocyte, Neutrophil and then applied convolutional neural network based models for the detection of types of blood cells with photographs involved in structured analysis. It described the innovative solution that provides efficient classification detection and deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various types of blood cells. A variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available blood cells given image dataset. So, it observed that neural networks can capture the colors and textures of lesions specific to respective type, which resembles human decision-making. And this model to deploy Django web framework.. We experi