Optimized deep learning networks for accurate identification of cancer cells in bone marrow

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50021882" target="_blank" >RIV/62690094:18470/25:50021882 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0893608024007469" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0893608024007469</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neunet.2024.106822" target="_blank" >10.1016/j.neunet.2024.106822</a>

Result language

angličtina

Original language name

Optimized deep learning networks for accurate identification of cancer cells in bone marrow

Original language description

Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential. Bones that have cancer have a different texture than bones in unaffected areas. Diagnosing hematological illnesses relies on correct labeling and categorizing nucleated cells in the bone marrow. However, timely diagnosis and treatment are hampered by pathologists&apos; need to identify specimens, which can be sensitive and time-consuming manually. Humanity&apos;s ability to evaluate and identify these more complicated illnesses has significantly been bolstered by the development of artificial intelligence, particularly machine, and deep learning. Conversely, much research and development is needed to enhance cancer cell identification—and lower false alarm rates. We built a deep learning model for morphological analysis to solve this problem. This paper introduces a novel deep convolutional neural network architecture in which hybrid multi-objective and category-based optimization algorithms are used to optimize the hyperparameters adaptively. Using the processed cell pictures as input, the proposed model is then trained with an optimized attention-based multi-scale convolutional neural network to identify the kind of cancer cells in the bone marrow. Extensive experiments are run on publicly available datasets, with the results being measured and evaluated using a wide range of performance indicators. In contrast to deep learning models that have already been trained, the total accuracy of 99.7% was determined to be superior.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Networks

ISSN

0893-6080

e-ISSN

1879-2782

Volume of the periodical

181

Issue of the periodical within the volume

January

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

"Article number: 106822"

UT code for WoS article

001346683800001

EID of the result in the Scopus database

2-s2.0-85207648346