Segmentation of Brain Tumor from Medical Images with Novel U-Shaped Encoder Decoder Architecture

Abstract

One of the Challenging tasks in medical field and computer vision is automatic brain segmentation with MRI (Magnetic Resonance Images). From the literature, the importance of deep neural networks is cleared as they have provided effective results in brain tumor segmentation problem in terms of accuracy and time. Mostly the training time is issued due to image features and for this purpose extra computational power is required to train the neural network model. The gradient problem is overcome in this study to fine tune the Novel unit model. CNNs & U-Shaped encoder decoder architectures produce effective result than other neural networks in terms of accuracy and time. The comparison is also performed in this study to show the robustness of U- Shaped encoder decoder architecture. Novel encoder and decoder model accuracy is 0.947 %that is better than other neural networks e.g., CNNs. Further this model is roughly three time faster than other models in terms of training time that's why less computation power is required to train this model.