  Human blood primarily comprises plasma, red blood cells, white blood cells,
and platelets. It plays a vital role in transporting nutrients to different
organs, where it stores essential health-related data about the human body.
Blood cells are utilized to defend the body against diverse infections,
including fungi, viruses, and bacteria. Hence, blood analysis can help
physicians assess an individual's physiological condition. Blood cells have
been sub-classified into eight groups: Neutrophils, eosinophils, basophils,
lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and
metamyelocytes), erythroblasts, and platelets or thrombocytes on the basis of
their nucleus, shape, and cytoplasm. Traditionally, pathologists and
hematologists in laboratories have examined these blood cells using a
microscope before manually classifying them. The manual approach is slower and
more prone to human error. Therefore, it is essential to automate this process.
In our paper, transfer learning with CNN pre-trained models. VGG16, VGG19,
ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2, and DenseNet-20
applied to the PBC dataset's normal DIB. The overall accuracy achieved with
these models lies between 91.375 and 94.72%. Hence, inspired by these
pre-trained architectures, a model has been proposed to automatically classify
the ten types of blood cells with increased accuracy. A novel CNN-based
framework has been presented to improve accuracy. The proposed CNN model has
been tested on the PBC dataset normal DIB. The outcomes of the experiments
demonstrate that our CNN-based framework designed for blood cell classification
attains an accuracy of 99.91% on the PBC dataset. Our proposed convolutional
neural network model performs competitively when compared to earlier results
reported in the literature.
