Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network

Abstract

Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Significant research has been undertaken in the area of AL diagnosis automation in order to deliver an accurate diagnosis. This study addresses the morphological classification of atypical white blood cells (WBCs), including immature WBCs and atypical lymphocytes, in acute myeloid leukemia (AML), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features. Using a hybrid model based on geometric transformation (GT) and a deep convolutional autoencoder (DCAE), this work provides a novel technique in the field of AI for resolving the issue of imbalanced distribution of WBCs in blood samples, nicknamed the “GT-DCAE WBC augmentation model”. In addition, to extract context-free atypical WBC features, this study develops a stable learning paradigm by incorporating WBC segmentation into deep learning. In order to classify atypical WBCs into eight distinct subgroups, a hybrid multiclassification model termed the “two-stage DCAE-CNN atypical WBC classification model” (DCAE-CNN) was developed. The model achieved an average accuracy of 97%, a sensitivity of 97%, and a precision of 98%. Overall and by class, the model’s discriminating abilities were exceptional, with an AUC of 99.7% and a class-wise range of 80% to 100%.

Deep learning model for differentiating acute myeloid and lymphoblastic leukemia in peripheral blood cell images via myeloblast and lymphoblast classification

“…Previous automated diagnostic machine learning models have predominantly focused on distinguishing between normal and immature WBCs in a binary system, yielding favorable performances. 17 , 26 , 27 The accuracy of discriminating ALL cells from normal cells ranges from 89.8% to 98%. 28 In our study, we developed a comprehensive cell-discrimination model encompassing 12 types of leukocytes in the peripheral blood of patients with AL.…”

Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

“…Li and Liu [31] employed the color invariance technique to fashion a trainable convolutional layer, which improved the performance of white blood cells classification. Elhassan et al [32] developed a two-stage hybrid model based on deep convolutional neural network to classify atypical white blood cells in acute myeloid leukemia, which achieved an average accuracy of 97% as reported.…”

The detection and classification of acute myeloid leukaemia blood cell images based on different YOLO approaches

“…The analysis of Table 4 indicates that our proposed YOLOv4-tiny model outperforms other models in terms of accuracy. [15] Two-stage hybrid model 97.00 Bairaboina and Battula, 2023 [38] Ghost-ResNeXt 98.61 This study YOLOv4-tiny 99.26…”

“…Rastogi et al [12] utilized LeuFeatx features with extra trees classifier, achieving an overall accuracy of 96.15%. In paper [15] and [38] used two-stage hybrid model and ghost-ResNeXt, achieving overall accuracies of 97.00% and 98.61%, respectively. In this paper, we propose to use the different YOLO models for blood cell detection and classification.…”