Blast cell segmentation and leukemia classification using hybrid Deep Kronecker WideResNet using blood smear images

Acute Lymphoblastic Leukemia (ALL) is a dangerous form of leukemia, which disturbs the bone marrow and irregular White Blood Cells (WBC) for the persons of all age groups. Early diagnosis of leukemia is necessary to provide appropriate care and cure the patients. Still, the detection of ALL from Peripheral Blood Smear (PBS) images is complicated due to the size and shape of the cells. This variability creates more difficulty for the segmentation and classification of Leukemia. To solve these issues, the Deep Kronecker Wide Residual Network (DKWRN) is developed for classifying Leukemia. The blood smear images are preprocessed using adaptive Gaussian filtering; hence, the noise from the image is diminished. After that, RefineNet effectively segments the Blast cell from the image. Then, augmentation such as flipping, resizing, and rotation enhances the dimension of the image. Following augmentation, essential features like Binary Pattern of Phase Congruency (BPPC) with Discrete Cosine Transform (DCT), and statistical features are extracted. Finally, the DKWRN classifies the Leukemia into early Pre-B, Pre-B, Pro-B, and Hematogones. Here, the proposed DKWRN is the integration of WideResNet (WRN) and Deep Kronecker Network (DKN). Furthermore, the proposed model offered optimal accuracy, True Negative Rate (TNR), recall, precision, and F1-score of 92.12 %, 91.40 %, 90.36 %, 91.56 %, and 90.96 %, respectively. Furthermore, the proposed DKWRN model demonstrated notable enhancements in classification accuracy when compared to existing techniques. Specifically, its performance exceeded that of Bayesian-based optimized convolutional neural network (CNN) for ALL detection (BO-ALLCNN) by 5.91 %, Support vector machine (SVM) by 4.63 %, ResNet-18 combined with SVM by 4.04 %, Residual Convolutional Neural Network (ResNet-152) by 2.27 %, DKN by 1.51 %, and WRN by 0.55 %, respectively.