Classification of Medical Images by Deep Neural Networks

As a result of the research, the model combining the ResNet-50 architecture and the pre-trained BERT language model that has achieved the following indicators: Accuracy 0.94, AUROC 0.87, and F1‑score 0.90 has been developed.

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Publication Details

Journal
Optics and Spectroscopy
Published
2024-12-01
DOI
https://doi.org/10.1134/s0030400x25700018
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Classification of Medical Images by Deep Neural Networks

Д. И. Львов, Ramil Zagidullin, Sergey Voinash, A. N. Khaibullin
Optics and Spectroscopy
Brain Tumor Detection and Classification
article

Classification of Medical Images by Deep Neural Networks

Д. И. Львов, Ramil Zagidullin, Sergey Voinash, A. N. Khaibullin
article en

Abstract

As a result of the research, the model combining the ResNet-50 architecture and the pre-trained BERT language model that has achieved the following indicators: Accuracy 0.94, AUROC 0.87, and F1‑score 0.90 has been developed.

Optics and SpectroscopyVol. 132(6-12)
ITMO University (RU), Kazan Federal University (RU)
Openalex Percentile: Top 67%
Brain Tumor Detection and Classification
DOISourceOpenAlex
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Classification of Medical Images by Deep Neural Networks — Д. И. Львов, Ramil Zagidullin, et al. · Optics and Spectroscopy (2024) | TGRS Research Map | TGRS