A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Popis výsledku v původním jazyce

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&apos;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 &amp; Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

Název v anglickém jazyce

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Popis výsledku anglicky

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&apos;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 &amp; Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

CAAI Transactions on Intelligence Technology

ISSN

2468-6557

e-ISSN

—

Svazek periodika

Neuveden

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

001136049800001

EID výsledku v databázi Scopus

2-s2.0-85181243626