Pathological Analysis of Blood Cells Using Deep Learning Techniques

TL;DR

This paper presents a neural network model for classifying blood cell images with high accuracy, aiming to assist pathologists and reduce human error in medical diagnostics.

Contribution

A novel neural network architecture for blood cell classification that outperforms existing models with 95.24% accuracy.

Findings

Achieved 95.24% classification accuracy.

Outperforms existing standard architectures.

Reduces human error in pathology labs.

Abstract

Pathology deals with the practice of discovering the reasons for disease by analyzing the body samples. The most used way in this field, is to use histology which is basically studying and viewing microscopic structures of cell and tissues. The slide viewing method is widely being used and converted into digital form to produce high resolution images. This enabled the area of deep learning and machine learning to deep dive into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the blood images with an accuracy of 95.24%. The performance of proposed model is better than existing standard architectures and work done by various researchers. Thus model will enable development of pathological system which will reduce human errors and daily load on laboratory men. This will in turn help pathologists in carrying out their work more efficiently and effectively.