Blood is a fluid that transports oxygen, providing energy to body cells that then produce carbon dioxide. It also plays a pivotal role in the immune system; blood circulating in living organisms contains 55% plasma, 40% red cells, 4% platelets, and 1% white blood cells (WBCs) [1]. The five primary types of WBCs are acidophilus, lymphocytes, monocytes, basophils, and neutrophils. These blood cells contain nuclei that differ from those of other cells [2]. WBC abnormalities are diagnosed by a blood smear test. Peripheral blood analysis is utilized for detection of diseases, such as malaria, leukemia, and anemia [3,4]. Such disorders are revealed by an increase and decrease the number of WBCs in the human body. Variations occur in the morphological structure of blood cells in terms of color, shape, and size, and such variations aid in the diagnosis of abnormalities in the WBCs [5]. Thus, segmentation and classification methods are used for the detection of WBCs. The manual evaluation of WBCs is laborious and time consuming [6], and computerized methods are a useful alternative that also minimize the workload of hematologists [7]. Segmentation and classification of WBCs are performed using conventional and deep learning methodologies. In conventional approaches, features are extracted manually; however, in deep learning, images features are learned automatically through a pipeline to improve efficiency [8]. In this study, an automated approach based on deep learning is proposed to segment and classify WBCs more accurately. The foremost contributions of the proposed work are as follows:

The Open Neural Network Exchange (ONNX) is applied with a YOLOv2 model, which detects the different types of WBCs. The features are extracted using activation-5 of the ONNX model. The extracted features are fed to the YOLOv2 model. The proposed framework accurately detects the region of interest (ROI).

In the literature, significant work has been done for the detection of WBCs, and some of the recent works are discussed in this section [9,10]. The detection of WBCs comprises four primary steps: pre-processing, localization/segmentation, extracting discriminant features, and classification. Pre-processing is a crucial step that is performed for noise removal and eradicating unwanted distortion to enhance the lesion region used in the subsequent segmentation step [11]. Segmentation is another vital step; it is used to group the homogeneous pixels and segment the required region from the input images. WBC cells are difficult to segment because of variations in their appearance [12]. Traditionally, WBCs were detected manually by pathologists, which is time-consuming and can be inaccurate [13]. Recently, automated approaches have been used for the detection of WBCs. Unsupervised clustering methods [14], thresholding approaches [15], shape-based approaches [16], and saliency-based models [17] are commonly used to localize WBCs. Watershed and histogram orientation approaches are used for the segmentation of WBCs. A large amount of data is presented into a set of vectors in the feature extraction process [18]. Selection of the optimum diagnostic features is an important task for the detection of WBCs [19]. Several types of features with different classifiers were used to differentiate the types of WBCs [20]. Supervised methods, such as SVM, Bayesian, random forest [21], and Bayesian [22], are used for the classification of WBCs. However, even the best feature extraction and selection methods struggle with accurate classification” or something similar [23]. Deep learning (DL) approaches are used widely to extract high-level information automatically [24] for the detection of ROIs, such as in WBC detection and classification [25]. Contour aware neural networks are used to segment the WBCs. Pixel by pixel classification is performed using a fully convolutional neural network (FCN) [26]. Mask R-CNN exhibits better classification as compared with other DL techniques [27].

The proposed approach comprises localization, segmentation, high-level feature extraction/selection, and classification steps for the analysis of WBCs. In the proposed approach, WBCs are detected/localized using ONNX as the backbone of YOLOv2. The localized cells are segmented using the proposed 3-D semantic segmentation model. Finally, the WBCs are classified using multi-SVM. An overview of the proposed method is presented in Fig. 1.

In this research, three publicly available benchmark datasets are used for the method evaluation. ALL-IDB1 contains 107 blood smear images, of which 33 are blasts and 74 are non-blast cells, and ALL-IDB2 contains 260 blood smear images, comprising 130 blasts and 130 non-blast cells [28–31]. The LISC dataset contains blood smear images of WBCs, including eosinophils, neutrophils, monocytes, lymphocytes, and basophils. The numbers of images for each type of WBC are not equal. To balance the different types of imaging data of WBCs, data augmentation is performed by rotating the images at different angles, such as 45∘, 90∘, 180∘, and 360∘. After augmentation, 6250 images of five types of WBCs are obtained, with each type having 1250 blood smear images [32].

In this study, deep learning approaches are proposed for the detection of WBCs. Detecting WBCs is challenging because blood smear images contain different color distributions in the cytoplasm and nucleus regions, making it difficult to segment these regions accurately. A 3-D semantic segmentation model is proposed, in which deeplabv3 is used as a bottleneck and the Xception model is used as a classification head to accurately segment the WBCs. Feature extraction/selection is another challenge for the classification of WBCs. The features are extracted from the pre-trained darknet-53 model, and informative features are selected using Bhattacharyya separability criteria and passed to the SVM with different types of kernels, namely cubic, quadratic, and optimized. The proposed classification method achieved an accuracy of 99.57% on the ALL-IDB1 dataset, 98.25% for the ALL-IDB2 dataset, and 98.4% for LISC datasets using the optimizable SVM kernel. The overall experimental outcomes demonstrate that the proposed technique achieved competitive outcomes by optimizing the SVM kernel. The proposed new framework based on a CNN can be used for the detection of different types of cancer, such as lung and bone cancer. It detects and classifies leukocytes at an early stage, thereby increasing the survival rate of patients.