An intelligent white blood cell detection and multi-class classification using fine optimal DCRNet

The major goal of this research is to develop a Deep Learning (DL) based automatic identification and classification of white blood cells (WBCs) with high accuracy and efficiency. The first phase of research is pre-processing and is accomplished by the Improved Median Wiener Filter (IMWF), which effectively eliminates the noises. The image is resized into a standard image size before filtering. The segmentation process takes place using Color Balancing Binary Threshold (CBBT) algorithm to divide the WBCs and another non-relevant background to improve the classification performance. The features like shape, texture and color of the WBCs are extracted from the segmented images. Finally, the classification takes place, and this is processed by a fine optimal deep convolution residual network (Fine Optimal DCRNet). In addition, the bionic model is introduced to improve classification accuracy. The dataset used in this research is BCCD and LISC datasets. The performance of the proposed model is validated using existing methods of Support Vector Machine (SVM), K-Nearest Neighbor (KNN), VGG-16, VGG-19, ResNet-50, DensetNet-121, DensetNet-169, Inception-V3, InceptionResNet-V2, Xception, MobileNet-224, Mobile NasNet, Tree, Naive Bayes, Ensemble active contour model, k-means clustering and handcraft and deep learned features-scale-invariant feature transform (HCDL-SIFT) in terms of Accuracy, Precision, Recall, Specificity, F-score, Relative Distance Error (RDE), Over-Segmentation Rate (OSR), Under-Segmentation Rate (USR) and Overall Error Rate (OER). For the LISC dataset, the detection model attains an outcome of 99%, 98%, 98%, 99%, 98%, 1.143, 0.0125, 0.056 and 0.125, respectively. For the BCCD dataset, apart from RDE, OSR, USR and OER metrics, the performance is evaluated as 98%, 96%, 98%, 99% and 97%.