Classification of All Blood Cell Images using ML and DL Models Article Swipe

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Rabia Asghar , Sanjay Kumar , Abeera Mahfooz · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2308.06300

Human blood primarily comprises plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting nutrients to different organs, where it stores essential health-related data about the human body. Blood cells are utilized to defend the body against diverse infections, including fungi, viruses, and bacteria. Hence, blood analysis can help physicians assess an individual's physiological condition. Blood cells have been sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets or thrombocytes on the basis of their nucleus, shape, and cytoplasm. Traditionally, pathologists and hematologists in laboratories have examined these blood cells using a microscope before manually classifying them. The manual approach is slower and more prone to human error. Therefore, it is essential to automate this process. In our paper, transfer learning with CNN pre-trained models. VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2, and DenseNet-20 applied to the PBC dataset's normal DIB. The overall accuracy achieved with these models lies between 91.375 and 94.72%. Hence, inspired by these pre-trained architectures, a model has been proposed to automatically classify the ten types of blood cells with increased accuracy. A novel CNN-based framework has been presented to improve accuracy. The proposed CNN model has been tested on the PBC dataset normal DIB. The outcomes of the experiments demonstrate that our CNN-based framework designed for blood cell classification attains an accuracy of 99.91% on the PBC dataset. Our proposed convolutional neural network model performs competitively when compared to earlier results reported in the literature.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2308.06300

PDF

https://arxiv.org/pdf/2308.06300

OA Status

green

Cited By

2

Related Works

10

OpenAlex ID

https://openalex.org/W4385848370

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OpenAlex ID

https://openalex.org/W4385848370

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DOI

https://doi.org/10.48550/arxiv.2308.06300

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Title

Classification of All Blood Cell Images using ML and DL Models

Work title

Type

preprint

OpenAlex work type

Language

en

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Publication year

2023

Year of publication

Publication date

2023-08-11

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Authors

Rabia Asghar, Sanjay Kumar, Abeera Mahfooz

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Landing page

https://arxiv.org/abs/2308.06300

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PDF URL

https://arxiv.org/pdf/2308.06300

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2308.06300

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Concepts

Convolutional neural network, Transfer of learning, Platelet, Blood cell, Artificial intelligence, White blood cell, Deep learning, Human blood, Residual neural network, Computer science, Pattern recognition (psychology), Immunology, Medicine, Physiology

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Cited by

2

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2025: 2

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Related works (count)

10

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