A modified convolutional neural network for tumor segmentation in multimodal brain magnetic resonance images Available to Purchase

Patients with this malignancy have a few decades less time left in their lives. If a brain tumour is discovered too late, the patient may go into a coma or become comatose and die. It is essential to identify at an early stage and segment brain tumour regions in MRI In order to perform clinical assessments such as, diagnosis, tumour detection treatment planning, and evaluation,. For segmenting brain tumours in MRI images, a better encoder-decoder convolutional neural network (CNN) architecture is proposed in this paper. Three encoding processing blocks are initially used to extract the hierarchy of tumor tissues characteristics from each input slice. Then, in the following three decoder blocks, these features are enhanced to obtain the original image size. Finally, the extracted features are classified using softmax classification. Concatenated feature maps are used in the decoding stages of this encoder-decoder CNN-based segmentation method, which calls for more processing power. For the BRATS-2018 dataset this technique obtains 91%, 93%, and 94% of dice score values.