A Framework for Segmentation and Classification of Blood Cells Using Generative Adversarial Networks

Abstract

Blood smear analysis is often used to diagnose diseases like malaria, Anemia, Leukemia, etc. Morphological changes, such as size, shapes, and color, are receiving much attention in pathological analysis. Existing methods for detecting, diagnosing and analyzing blood smears cannot quantify overlapped, irregular boundaries and complex structures. This work proposes and evaluates a framework that utilizes Generative adversarial networks (GANs) for the segmentation and classification of blood elements, that is, white blood cells (WBCs), red blood cells (RBCs), and platelets (PLTs) simultaneously. The Generator of the network determines the mapping from microscopic images of blood cells to a confidence map. This mapping stipulates the probabilities of the pixel of the microscopic blood cell images with respect to ground truth. The Discriminator of the network is essential to castigate the mismatch between the microscopic blood cells images and confidence map. Additionally, adversarial learning enables the Generator to generate a qualitative confidence map that is converted into segmented images. We have calculated minimum, maximum, and average losses to judge the performance of the proposed model. We measure structural similarity, peak signal-to-noise ratio, pixel classification error, and finally, classified cells. The proposed framework can analyze all the blood cell elements simultaneously. The proposed framework shows a significant improvement in the segmentation and classification of blood cell elements compared to state-of-the-art techniques. During the training process, generator total loss reduces by 12.18%, 5.39%, and 3.62% for RBCs, WBCs, and PLTs, respectively. Our results demonstrate that the proposed framework outperforms existing state-of-the-art techniques, achieving the highest pixel correctly classified (PCC) ratio for the segmentation of blood cells as 99.8%, 93.4%, and 99.9% for WBCs, RBCs, and PLTs, respectively. Our framework attains 95.45% and 88.89% classification accuracy for WBCs on ALL-IDB-I and ALL-IDB-II datasets. The dataset used for this study can be found at https://drive.google.com/drive/folders/1F7kZ1SRWUD9R6aHLMkj3wsjcHnvlGuwP?usp=sharing

Keywords

• Segmentation
• classification
• convolutional neural network
• generator
• discriminator
• generative adversarial network