Abstract:
Human immune system contains white blood cells (WBC) that are good
indicator of many diseases like bacterial infections, AIDS, cancer, spleen,
etc. White blood cells have been sub classified into four types: monocytes,
lymphocytes, eosinophils and neutrophils on the base of their nucleus,
shape and cytoplasm. Traditionally in laboratories, pathologists and
hematologists analyze these blood cells through microscope and then
classify them manually. This manual process takes more time and
increases the chance of human error. Hence, there is a need to automate
this process. We have first applied different CNN models, InceptionV3,
VGG16, MobileNetV2, LeNet and ResNet50 to automatically classify the
white blood cells. These CNN models are applied on Kaggle dataset of
microscopic images. Although we achieved reasonable accuracy ranging
between 92 to 95%, still there is need to enhance the performance. Hence,
inspired by these architectures a framework has been proposed to
automatically classify the four types of white blood cells with increased
accuracy. The aim is to develop a convolution neural network (CNN)
based classification system with decent generalization ability. The
proposed CNN model has been tested on white blood cells images from
Kaggle and LISC datasets. Accuracy achieved is 99.57% and 98.67% for
both datasets respectively. Our proposed convolutional neural networkbased model provides competitive performance as compared to previous
results reported in literature.
