Red blood cell segmentation and classification using hybrid image processing-squeezenet model

In medical diagnostics, blood testing is considered to be one of the most important clinical examination tests. Manual microscopic inspection of blood cells is time-consuming and subjective. Therefore, an automated blood cell classification system that will help a pathologist to identify the components of blood and diagnose the diseases pertaining to those cells, in a fast and efficient manner is useful. Due to multiple variable factors such as cell types, different stains and magnifications, and data complexities such as cell overlapping, inhomogeneous intensities, background clutters and image artifacts, development of a model for automated diagnosis of blood cells is an arduous task. This paper presents a robust and accurate method of segmenting and classifying the blood cells in Peripheral Blood Smear (PBS) images. The method involves a pre-processing step consisting of Decorrelation Stretching (DCS), followed by histogram matching for stain normalization and a Fuzzy C-means clustering algorithm for the segmentation of Red Blood Cells (RBCs). The segmented blood cells were then counted and classified as normal and abnormal along with the type of abnormalities using the SqueezeNet Deep Learning (DL) model which offered an average classification accuracy of 97.9%.