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Licensed Unlicensed Requires Authentication Published by De Gruyter October 5, 2022

An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification

  • Silambarasi Palanivel EMAIL logo and Viswanathan Nallasamy

Abstract

Objectives

The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications.

Methods

The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells.

Results

After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%.

Conclusions

Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.


Corresponding author: Silambarasi Palanivel, Department of Electronics and Communication Engineering, Mahendra Engineering College for Women, Tamil Nadu, India, E-mail:

  1. Research funding: None declared.

  2. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

  4. Informed consent: None declared.

  5. Ethical approval: None declared.

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Received: 2022-08-01
Accepted: 2022-09-11
Published Online: 2022-10-05
Published in Print: 2023-04-25

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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