ArticleBioengineering (Basel, Switzerland)2025
Enhancing Blood Cell Diagnosis Using Hybrid Residual and Dual Block Transformer Network.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Automated leukemia detection from microscopic images using deep transfer learning with explainable AI-based analysis.Scientific reports · 2026Article
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- Revolutionizing AMD detection Bi model CNNs and hybrid feature selection for automated grading.Scientific reports · 2025Article
- WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules.Scientific reports · 2025Article
- AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology.Journal of imaging · 2025Article
- LN-DETR: cross-scale feature fusion and re-weighting for lung nodule detection.Scientific reports · 2025Article
- A novel deep learning based approach with hyperparameter selection using grey wolf optimization for leukemia classification and hematologic malignancy detection.PeerJ. Computer science · 2025Article
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Authors and funding
4 authors.
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Abstract
Leukemia is a life-threatening blood cancer that affects a large cross-section of the population, which underscores the great need for timely, accurate, and efficient diagnostic solutions. Traditional methods are time-consuming, subject to human vulnerability, and do not always grasp the subtle morphological differences that form the basic discriminatory features among different leukemia subtypes. The proposed residual vision transformer (ResViT) model breaks these limitations by combining the advantages of ResNet-50 for high dimensional feature extraction and a vision transformer for global attention to the spatial features. ResViT can extract low-level features like texture and edges as well as high-level features like patterns and shapes from the leukemia cell images. Furthermore, we designed a dual-stream ViT with a convolution stream for local details and a transformer stream for capturing the global dependencies, which enables ResViT to pay attention to multiple image regions simultaneously. The evaluation results of the proposed model on the two datasets were more than 99%, which makes it an excellent candidate for clinical diagnostics.
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