Leukemia Disease Classification using Concatenated Convolutional Neural Network with Vision Transformer

One of the lethal diseases that has a high mortality rate in both adults and children is acute lymphoblastic leukemia (ALL). A clinical pathologist examines the microscopic pictures of white blood cells to get the traditional diagnosis of this illness. Nevertheless, this method is based on manual observation and frequently yields unreliable outcomes. This study suggests combining a convolutional neural network (CNN) approach with a vision transformer (ViT) to create an automated system for diagnosing acute lymphoblastic leukemia. This study was conducted using the ALL IDB databases for this purpose. However, to address the overfitting issue in the model, data augmentation approaches have been used to create images. Using 8 heads and 8 transformer layers, the Concatenated CNN-ViT model produced encouraging results with a projection dimension of 32. Nonetheless, the outcomes demonstrated that, in the diagnosis of ALL, the proposed model achieved $\mathbf{9 8. 6 2 \%}$ accuracy, $\mathbf{9 8. 6 6 \%}$ precision, $\mathbf{9 8. 4 1 \%}$ recall, and $\mathbf{9 8. 5 3 \%}$ F1-score. The high accuracy indicates that compared to other studies published in the same field, it offers a more efficient method of diagnosing acute lymphoblastic leukemia.