ArticleComputational intelligence and neuroscience2022
Deep Learning Model for the Automatic Classification of White Blood Cells.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 23 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- Attention-Based Dual-Path Deep Learning for Blood Cell Image Classification Using ConvNeXt and Swin Transformer.Journal of imaging informatics in medicine · 2026Article
- Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN.Scientific reports · 2025Article
- Acute Myeloid Leukemia subtype classification using single blood cell images.Discover oncology · 2025Article
- Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement.Scientific reports · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- An explainable AI-based blood cell classification using optimized convolutional neural network.Journal of pathology informatics · 2024Article
- Article
- White blood cells classification using multi-fold pre-processing and optimized CNN model.Scientific reports · 2024Article
- Article
- An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.Frontiers in oncology · 2024Article
- Leukocyte subtype classification with multi-model fusion.Medical & biological engineering & computing · 2023Article
- An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization.Diagnostics (Basel, Switzerland) · 2023Article
- Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection.Cancers · 2023Article
- DLBCNet: A Deep Learning Network for Classifying Blood Cells.Big data and cognitive computing · 2023Article
- A deep learning model for detection of leukocytes under various interference factors.Scientific reports · 2023Article
- White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization.Diagnostics (Basel, Switzerland) · 2023Article
- Retracted: Deep Learning Model for the Automatic Classification of White Blood Cells.Computational intelligence and neuroscience · 2023Article
- White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.Healthcare (Basel, Switzerland) · 2022Article
- Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images.Frontiers in oncology · 2022Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images.
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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.