ArticleDiagnostics (Basel, Switzerland)2023
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.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Advances in Leukemia detection and classification: A Systematic review of AI and image processing techniques.F1000Research · 2024Pooled it
- Aligning deep learning and interpretable models for blood cell classification: A dual-model framework for explainability.PLOS digital health · 2026Article
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Hybrid handcrafted and deep feature fusion for automated acute myeloid leukemia classification using TCMA-Net on a class-balanced dataset.Frontiers in oncology · 2026Article
- Development of criteria to optimize manual smear review of automated complete blood counts using a machine learning model.Veterinary clinical pathology · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- An explainable AI-based blood cell classification using optimized convolutional neural network.Journal of pathology informatics · 2024Article
- Article
- An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2024Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- Article
- Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network.Scientific reports · 2023Article
- Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.