A brain tumor is an unnatural expansion of brain cells that can't be stopped, making it one of the deadliest diseases of the nervous system. The brain tumor segmentation for its earlier diagnosis is a difficult task in the field of medical image analysis. Earlier, segmenting brain tumors was done manually by radiologists but that requires a lot of time and effort. Inspite of this, in the manual segmentation there was possibility of making mistakes due to human intervention. It has been proved that deep learning models can outperform human experts for the diagnosis of brain tumor in MRI images. These algorithms employ a huge number of MRI scans to learn the difficult patterns of brain tumors to segment them automatically and accurately. Here, an encoder-decoder based architecture with deep convolutional neural network is proposed for semantic segmentation of brain tumor in MRI images. The proposed method focuses on the image downsampling in the encoder part. For this, an intelligent LinkNet-34 model with EfficientNetB7 encoder based semantic segmentation model is proposed. The performance of LinkNet-34 model is compared with other three models namely FPN, U-Net, and PSPNet. Further, the performance of EfficientNetB7 used as encoder in LinkNet-34 model has been compared with three encoders namely ResNet34, MobileNet_V2, and ResNet50. After that, the proposed model is optimized using three different optimizers such as RMSProp, Adamax and Adam. The LinkNet-34 model has outperformed with EfficientNetB7 encoder using Adamax optimizer with the value of jaccard index as 0.89 and dice coefficient as 0.915.
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