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Deep Convolutional Neural Network for Brain Tumor Segmentation

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Abstract

Glioma is one of the primary brain tumors, and it grows within the brain substance. The manual segmentation of gliomas takes more time and may involve human mistakes. This study proposes automatic segmentation based on a deep neural network (DNN). It reduces computational time and extends the survival rate due to prior treatment plans. The proposed Double ConvNet (DCN) architecture segments gliomas from 2D multi-modal images of magnetic resonance imaging (MRI). It contains two different U-Net architectures. Net-1 provides easier optimization than plain deep networks and eliminates degradation issues. Net-2 compiles multi-scale contextual information and generates more detailed information. The proposed model improves performance compared to state-of-the-art models.

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  1. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, 400 feet outer ring road, Avadi, Chennai, Tamilnadu, 600062, India

    K. Sambath Kumar

  2. Department of Electronics and Communication Engineering, Karpagam College of Engineering, Myleripalayam village, Othakalmandapam, Coimbatore, Tamilnadu, 641032, India

    A. Rajendran

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  1. K. Sambath Kumar
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Kumar, K.S., Rajendran, A. Deep Convolutional Neural Network for Brain Tumor Segmentation. J. Electr. Eng. Technol. 18, 3925–3932 (2023). https://doi.org/10.1007/s42835-023-01479-y

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