A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10253880" target="_blank" >RIV/61989100:27240/24:10253880 - isvavai.cz</a>
Result on the web
<a href="https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12276" target="_blank" >https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12276</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1049/cit2.12276" target="_blank" >10.1049/cit2.12276</a>
Alternative languages
Result language
angličtina
Original language name
A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images
Original language description
Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly. (C) 2024 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - 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
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CAAI Transactions on Intelligence Technology
ISSN
2468-6557
e-ISSN
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Volume of the periodical
Neuveden
Issue of the periodical within the volume
2024
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
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UT code for WoS article
001136049800001
EID of the result in the Scopus database
2-s2.0-85181243626