ArticleBig data and cognitive computing2023
DLBCNet: A Deep Learning Network for Classifying Blood Cells.
Article in Big data and cognitive computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Diagnosing orthopaedic infection by identifying neutrophils in whole histology slide images with machine learning trained on publicly available datasets.Bone & joint research · 2026Article
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- Parameter efficient fine-tunning of foundation model to facilitate tumor response prediction for ovarian cancer patients.BMC medical imaging · 2025Article
- Continuous blood glucose monitoring prediction for diabetes using evolving neural network.Scientific reports · 2025Article
- Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.Scientific reports · 2025Article
- Machine learning framework for predicting susceptibility to obesity.Scientific reports · 2025Article
- Improved Inception-Capsule deep learning model with enhanced feature selection for early prediction of heart disease.Scientific reports · 2025Article
- Metaverse in dentistry: Bridging virtual innovation with real-world patient care and education.Bioinformation · 2025Article
- An explainable AI-based blood cell classification using optimized convolutional neural network.Journal of pathology informatics · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Background: Blood is responsible for delivering nutrients to various organs, which store important health information about the human body. Therefore, the diagnosis of blood can indirectly help doctors judge a person's physical state. Recently, researchers have applied deep learning (DL) to the automatic analysis of blood cells. However, there are still some deficiencies in these models. Methods: To cope with these issues, we propose a novel network for the multi-classification of blood cells, which is called DLBCNet. A new specifical model for blood cells (BCGAN) is designed to generate synthetic images. The pre-trained ResNet50 is implemented as the backbone model, which serves as the feature extractor. The extracted features are fed to the proposed ETRN to improve the multi-classification performance of blood cells. Results: The average accuracy, average sensitivity, average precision, average specificity, and average f1-score of the proposed model are 95.05%, 93.25%, 97.75%, 93.72%, and 95.38%, accordingly. Conclusions: The performance of the proposed model surpasses other state-of-the-art methods in reported classification results.
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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.