  Blood cell classification and counting are vital for the diagnosis of various
blood-related diseases, such as anemia, leukemia, and thrombocytopenia. The
manual process of blood cell classification and counting is time-consuming,
prone to errors, and labor-intensive. Therefore, we have proposed a DL based
automated system for blood cell classification and counting from microscopic
blood smear images. We classify total of nine types of blood cells, including
Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte,
Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like
image resizing, rescaling, contrast enhancement and augmentation are utilized.
To segment the blood cells from the entire microscopic images, we employed the
U-Net model. This segmentation technique aids in extracting the region of
interest (ROI) by removing complex and noisy background elements. Both
pixel-level metrics such as accuracy, precision, and sensitivity, and
object-level evaluation metrics like Intersection over Union (IOU) and Dice
coefficient are considered to comprehensively evaluate the performance of the
U-Net model. The segmentation model achieved impressive performance metrics,
including 98.23% accuracy, 98.40% precision, 98.25% sensitivity, 95.97%
Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a
watershed algorithm is applied to the segmented images to separate overlapped
blood cells and extract individual cells. We have proposed a BloodCell-Net
approach incorporated with custom light weight convolutional neural network
(LWCNN) for classifying individual blood cells into nine types. Comprehensive
evaluation of the classifier's performance is conducted using metrics including
accuracy, precision, recall, and F1 score. The classifier achieved an average
accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of
97.10%.
