White blood cells segmentation and classification using a random forest and residual networks implementation

Marlon Rodrigues Garcia; Erika Toneth Ponce Ayala; Sebastião Pratavieira; Vanderlei Salvador Bagnato

doi:10.1117/12.3007504

13 March 2024 White blood cells segmentation and classification using a random forest and residual networks implementation

Marlon Rodrigues Garcia, Erika Toneth Ponce Ayala, Sebastião Pratavieira, Vanderlei Salvador Bagnato

Author Affiliations +

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 128570M (2024) https://doi.org/10.1117/12.3007504
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Artificial intelligence algorithms are interesting solutions to automate the tedious manual counting of white blood cells by a specialist. Although interesting machine learning algorithms have been proposed for this task, there is a lack in the literature for high-accuracy methods (more than 99%) tested on larger datasets (more than 10 thousand images). This paper presents a segmentation and classification methodology, based on Random Forest and ResNet50, along with a comparison between ResNet models with different numbers of layers. The segmentation was tested in microscope-like images mounted using multiple single-cell images, widely available in online datasets, yielding 300×300 images to be classified by the residual network. For image classification, ResNet50 reached higher accuracies (99.3%, to the best of our knowledge, the higher accuracy for models with more than 1000 images), with the model size comparison pointing to model overfitting for larger models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Marlon Rodrigues Garcia, Erika Toneth Ponce Ayala, Sebastião Pratavieira, and Vanderlei Salvador Bagnato "White blood cells segmentation and classification using a random forest and residual networks implementation", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570M (13 March 2024); https://doi.org/10.1117/12.3007504

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available