leukovit: an efficient vision transformer-based model for automatic classification of leukocytes

The identification and evaluation of leukocytes are important to assess the quality of the human immune system; however, the analysis of blood smears depends on the pathologist’s expertise. the manual method for analyzing and classifying wbcs is costly and time-consuming and can result in errors in detection. most deep learning methods use cnn-based models for white blood cell classification. this paper discusses the use of a vit-based network, for the classification of leukocytes (wbcs) in a blood sample. the dataset used in this paper consists of 352 images with a size of 320x240, which was augmented through techniques to create a balanced dataset of 12444 images. the augmented data was then used to train a vit-based architecture to classify the different types of wbcs. as the first step of the proposed algorithm, a convolutional tokenizer has been applied for patch extraction of images. these patches have been flattened and have been used as input for a vit-based structure to recognize the subclasses in the second step. the results obtained using leukovit show that the accuracy of the proposed network is 99.04% which is outperforming the state-of-the-art networks.

leukovit: an efficient vision transformer-based model for automatic classification of leukocytes

the identification and evaluation of leukocytes are important to assess the quality of the human immune system; however, the analysis of blood smears depends on the pathologist’s expertise. the manual method for analyzing and classifying wbcs is costly and time-consuming and can result in errors in detection. most deep learning methods use cnn-based models for white blood cell classification. this paper discusses the use of a vit-based network, for the classification of leukocytes (wbcs) in a blood sample. the dataset used in this paper consists of 352 images with a size of 320x240, which was augmented through techniques to create a balanced dataset of 12444 images. the augmented data was then used to train a vit-based architecture to classify the different types of wbcs. as the first step of the proposed algorithm, a convolutional tokenizer has been applied for patch extraction of images. these patches have been flattened and have been used as input for a vit-based structure to recognize the subclasses in the second step. the results obtained using leukovit show that the accuracy of the proposed network is 99.04% which is outperforming the state-of-the-art networks.