Automatic classification of 10 blood cell subtypes using transfer learning via pre-trained convolutional neural networks

Abstract

Human blood is primarily composed of plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting oxygen and nutrients to all organs, and stores essential health-related data about the human body. Blood cells are utilized to defend the body against infections and disease. Hence, analysis of blood permits physicians to assess an individual's physiological condition. Blood cells are sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets (thrombocytes) on the basis of their nucleus, shape and cytoplasm. Traditionally, pathologists and hematologists have identified and examined these via microscopy prior to manual classification, with this manual approach being slow and prone to human error. Therefore, it is essential to automate this process. In the current study, transfer learning with a series of pre-trained Convolutional Neural Network (CNN) models—VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2 and DenseNet-201 was applied to the normal peripheral blood cells dataset (PBC). The overall accuracy achieved with individual CNNs ranged from 91.4 % to 94.7 %. Based on these pre-trained architectures, a CNN-based architecture has been developed to automatically classify all ten blood cell types. The proposed transfer learning CNN model was tested on blood cell images from the PBC, Kaggle and LISC datasets. Achieved accuracy was 99.91 %, 99.68 % and 98.79 %, respectively, across these three datasets. The presented CNN architecture outperforms all previous results reported in the scientific/medical literature with a high capacity for framework generalization in future applications of blood cell classification.