Classification of WBC Using Deep Learning for Diagnosing Diseases
Riya Roy, Swapna B. Sasi · 2018
The blood test plays vital role in recognizing diseases. It gives the details about the general state of a person's wellbeing conditions. Based on data from blood test specialists, they choose the treatment for the patient required. White blood cells (WBC) are an important component of blood system. It is essential for good health and protection against disease. WBC contains fundamentally five parts and relies on its variety of size, count, shape. Based on variation in these features there can occur many diseases. Presently, we have confronted many issues in blood testing. One common issue is that a unique blood was getting an alternate and extraordinary estimation of cell check from the diverse lab technician. Most of the laboratory follows the manual counting technique, which is very tedious and less accurate. The proposed frameworks help to classification of each kind of white blood cells utilizing multi class support vector machine classification and convolutional neural networks (CNN). Identifying the Neutrophils, Lymphocytes, Monocytes, Eosinophil and Basophils variation can be identified using deep learning for diagnosis diseases. Likewise, discover the level of malignant cell in blood and leukocytes for different age domain.