MRI based Brain Tumor Classification using Modified Convolutional Neural Network Model
Keywords:
Brain tumor, classification, convolution neural network, contour approximations, gaussian blur, magnetic resonance imaging and segmentationAbstract
: In recent decades, the Brain Tumor Classification (BTC) by utilizing Magnetic Resonance Imaging (MRI) is an emerging research topic. The existing detection models are complicated, due to the high similarity between normal and abnormal brain tissues and also it consumes an enormous amount of computational time. The automatic brain tumor detection utilizing MRI images is a challenging task for early clinical assessment and treatment planning. This publication proposes a new automatic model to address BTC problems. This study used MRI brain scans from the 2018–2020 Brain Tumor Segmentation (BRATS) databases. Further, the image denoising is performed utilizing Gaussian blur and contour approximation techniques. Finally, the denoised MRI brain images are given as the input to the Modified Convolutional Neural Network (CNN) for brain lesion segmentation and sub-types of tumor classification. The modified CNN model comprises the ReduceLRonPlateau function for early stopping criteria that decreases system complexity and error loss. The extensive experimental investigation demonstrated that the proposed modified CNN model has obtained 99.87%, 99.80%, and 99.63% of accuracy on the BRATS 2018, 2019, and 2020 databases with classification loss of 0.0198, where the obtained results are superior compared to the existing models.
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