skip to main content
Idioma:

Deep Learning Model for the Automatic Classification of White Blood Cells

Sharma, Sarang ; Gupta, Sheifali ; Gupta, Deepali ; Juneja, Sapna ; Gupta, Punit ; Dhiman, Gaurav ; Kautish, Sandeep Gupta, Suneet Kumar ; Suneet Kumar Gupta

Computational intelligence and neuroscience, 2022, Vol.2022, p.7384131-13 [Periódico revisado por pares]

United States: Hindawi

Texto completo disponível

Citações Citado por
  • Título:
    Deep Learning Model for the Automatic Classification of White Blood Cells
  • Autor: Sharma, Sarang ; Gupta, Sheifali ; Gupta, Deepali ; Juneja, Sapna ; Gupta, Punit ; Dhiman, Gaurav ; Kautish, Sandeep
  • Gupta, Suneet Kumar ; Suneet Kumar Gupta
  • Assuntos: Accuracy ; Algorithms ; Architecture ; Automation ; Blood ; Classification ; Data analysis ; Datasets ; Deep Learning ; Eosinophils ; Image acquisition ; Image classification ; Leukocytes ; Leukocytes (eosinophilic) ; Leukocytes (neutrophilic) ; Lymphocytes ; Machine learning ; Model accuracy ; Monocytes ; Neural networks ; Neutrophils
  • É parte de: Computational intelligence and neuroscience, 2022, Vol.2022, p.7384131-13
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    ObjectType-Correction/Retraction-3
    Academic Editor: Suneet Kumar Gupta
  • Descrição: 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.
  • Editor: United States: Hindawi
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.