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Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network
Badža, Milica M. ; Barjaktarović, Marko Č.
Applied sciences, 2020-03, Vol.10 (6), p.1999
[Periódico revisado por pares]
Basel: MDPI AG
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Título:
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network
Autor:
Badža, Milica M.
;
Barjaktarović, Marko Č.
Assuntos:
Accuracy
;
Algorithms
;
Architecture
;
Artificial intelligence
;
Biopsy
;
Brain architecture
;
Brain cancer
;
brain tumor classification
;
Brain tumors
;
Classification
;
convolutional neural network
;
Image classification
;
Image contrast
;
Image databases
;
Image enhancement
;
Image processing
;
Learning algorithms
;
Machine learning
;
Magnetic resonance imaging
;
Medical imaging
;
Neural networks
;
Tumors
É parte de:
Applied sciences, 2020-03, Vol.10 (6), p.1999
Descrição:
The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.
Editor:
Basel: MDPI AG
Idioma:
Inglês
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