Deep learning approach for segmentation and classification of blood cells using enhanced CNN

Abstract

The main aim of our proposed work is to segment and classify blood cells using K means algorithm, and also with the help of image processing techniques. A complete blood cell count is very important in the medical analysis for evaluating the total health condition of the body. In the olden days, the blood cells are counted manually with the help of a hem cytometer with other lab equipment and certain chemicals. This technique both time-consuming and challenging. Deep Learning (DL) is an artificial intelligence subset of machine learning that can examine unsupervised information. RBC, or red blood cells, are red cells that are numerous in the blood and are noted for their dark red colour. White blood cells, commonly known as leukocytes, protect the body from infection. Processing tools like MATLAB is used to find the variations in area, perimeter and statistical parameters like mean and standard deviation that separates white blood cells from other blood components. Because of its high accuracy, Enhanced CNN can be used in this study for the categorization and recognition of normal and abnormal blood cell images. Average accuracy of 95% and average precision 0f 0.93 which is higher than existing CNN.

Keywords

• Blood cells
• Convolutional neural network
• Deep learning
• Classification
• Segmentation