Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256803" target="_blank" >RIV/61989100:27240/24:10256803 - isvavai.cz</a>

Result on the web

<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0307825" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0307825</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0307825" target="_blank" >10.1371/journal.pone.0307825</a>

Result language

angličtina

Original language name

Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning

Original language description

Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnormal cell growth within the brain, exhibits various sizes and shapes, making their manual detection from magnetic resonance imaging (MRI) scans a subjective and challenging task for healthcare professionals, hence necessitating automated solutions. This study investigates the potential of deep learning, specifically the DenseNet architecture, to automate brain tumor classification, aiming to enhance accuracy and generalizability for clinical applications. We utilized the Figshare brain tumor dataset, comprising 3,064 T1-weighted contrast-enhanced MRI images from 233 patients with three prevalent tumor types: meningioma, glioma, and pituitary tumor. Four pre-trained deep learning models-ResNet, EfficientNet, MobileNet, and DenseNet-were evaluated using transfer learning from ImageNet. DenseNet achieved the highest test set accuracy of 96%, outperforming ResNet (91%), EfficientNet (91%), and MobileNet (93%). Therefore, we focused on improving the performance of the DenseNet, while considering it as base model. To enhance the generalizability of the base DenseNet model, we implemented a fine-tuning approach with regularization techniques, including data augmentation, dropout, batch normalization, and global average pooling, coupled with hyperparameter optimization. This enhanced DenseNet model achieved an accuracy of 97.1%. Our findings demonstrate the effectiveness of DenseNet with transfer learning and fine-tuning for brain tumor classification, highlighting its potential to improve diagnostic accuracy and reliability in clinical settings.

Czech name

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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

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

O - Projekt operacniho programu

Publication year

2024

Confidentiality

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

Name of the periodical

PLoS One

ISSN

1932-6203

e-ISSN

1932-6203

Volume of the periodical

19

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

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UT code for WoS article

001326653200061

EID of the result in the Scopus database

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