  The diagnosis of blood-based diseases often involves identifying and
characterizing patient blood samples. Automated methods to detect and classify
blood cell subtypes have important medical applications. Automated medical
image processing and analysis offers a powerful tool for medical diagnosis. In
this work we tackle the problem of white blood cell classification based on the
morphological characteristics of their outer contour, color. The work we would
explore a set of preprocessing and segmentation (Color-based segmentation,
Morphological processing, contouring) algorithms along with a set of features
extraction methods (Corner detection algorithms and Histogram of
Gradients(HOG)), dimensionality reduction algorithms (Principal Component
Analysis(PCA)) that are able to recognize and classify through various
Unsupervised(k-nearest neighbors) and Supervised (Support Vector Machine,
Decision Trees, Linear Discriminant Analysis, Quadratic Discriminant Analysis,
Naive Bayes) algorithms different categories of white blood cells to
Eosinophil, Lymphocyte, Monocyte, and Neutrophil. We even take a step forwards
to explore various Deep Convolutional Neural network architecture (Sqeezent,
MobilenetV1,MobilenetV2, InceptionNet etc.) without preprocessing/segmentation
and with preprocessing. We would like to explore many algorithms to identify
the robust algorithm with least time complexity and low resource requirement.
The outcome of this work can be a cue to selection of algorithms as per
requirement for automated blood cell classification.
