Abstract
Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.
Highlights
The method of distinguishing tumor borders from healthy cells is still a challenging mission in the medical habit
Brain tumors are the growth of abnormal cells or a mass in a brain
Several studies discussed the application of Convolutional Neural Network (CNN) model as a deep learning architecture for Magnetic resonance imaging (MRI)-based brain tumor segmentation by using magnetic resonance Fluid-attenuated inversion recovery (FLAIR) images [2], multimodal MRI scans [3,4,5], and automatic semantic segmentation [6]
Summary
The method of distinguishing tumor borders from healthy cells is still a challenging mission in the medical habit. Fluid-attenuated inversion recovery (FLAIR) and Magnetic resonance imaging (MRI) modalities can provide physicians with excellent information concerning tumor penetration [1]. Several studies discussed the application of Convolutional Neural Network (CNN) model as a deep learning architecture for MRI-based brain tumor segmentation by using magnetic resonance FLAIR images [2], multimodal MRI scans [3,4,5], and automatic semantic segmentation [6]. The study [7] presented 3D convolutional neural networks for tumor segmentation using long-range 2D context, which was updated to more accurately classify and detect brain cancer cells in MRI and computerized tomography (CT) images using nano-contrast agents [8], and using dense residual refine networks for automatic brain tumor segmentation in [9, 10]. Most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors
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More From: Eastern-European Journal of Enterprise Technologies
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