Brain tumor segmentation and classification approach for MR images based on convolutional neural networks
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Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Brain tumor considers one of the most dangerous types of cancers as it affects the main nervous system of the human body. Therefore, several techniques using computer vision has been proposed for early diagnoses and avoiding surgical intervention. However, these techniques face challenges in terms of segmentation and classification processes to detect the brain tumor within Magnetic resonance images (MRI). This paper proposes an automated system for detecting and classifying the brain tumor. This system composed of three phases is including enhancement, segmentation, and classification. The enhancement phase utilizes the Adaptive Histogram Equalization (AHE) in order to adjust the MRI images. The segmentation process was achieved using U-NET to segment the abnormal cells from normal brain tissue. The classification phase was conducted using 3D-CNN to classify the brain tumor into a High-Grade Glioma (HGG) and a Low-Grade-Glioma (LGG). Several experiments were conducted to validate the developed system using Brats-2015 dataset. The system achieved 99.7% (Dice Similarity Coefficient) DSC as an accurate rate for segmentation, and 96% and 98.5% an accurate rate using 5-fold and 10-fold, respectively. © 2020 IEEE.
Açıklama
1st International Conference Information Technology to Enhance E-Learning and other Application, IT-ELA 2020 -- 12 July 2020 through 13 July 2020 -- -- 164981
Anahtar Kelimeler
Classification, Convolutional Neural Network, Image Processing, Medical Image, Segmentation
Kaynak
Proceedings of 2020 1st Information Technology to Enhance E-Learning and other Application Conference, IT-ELA 2020
WoS Q Değeri
Scopus Q Değeri
N/A
