Image Classification of White Blood Cells using Deep Learning Models

Image Classification of White Blood Cells using Deep Learning Models

Lalith Samanthapuri, Sujay Bhagawan Ghadge, Vishaal Pedapatnam, Rama Krishna Chavali, Shashwat Mahodaya, Bathineni Taraka Rameswara K Durga Prasad, Varun Vallabhaneni, Nunna Karthik, Athota Koushik

Abstract

White blood cell classification is critical for medical diagnosis. In practice, a hematologist classifies white blood cells by carefully studying a small blood sample under a microscope. The current methods for detecting the subtypes f white blood cells are time-consuming and prone to inaccuracy. Computer-assisted white blood cell identificatin and diagnosis cut the time it takes to identify white blood cells in half and eliminate human error. In the realm of white blood cell classification, several deep learning algorithms have been created in recent years that can identify. It is easier for trained people to make conclusions about diseases after classified blood cells. Since deep learning models perate with large databases, training, and testing networks, consequently, when the model is trained with powerful computing skills accessible, distinct perfect models can be constructed and used in medical analysis and applications dealing with the number of white blood cells and subtypes of white blood cells.

Keywords: White blood cells, deep learning, Convolutional neural network, image classiffica- tion, image processing, neutrophils.