An improved DNN with FFCM method for multimodal brain tumor segmentation

Abstract

A brain tumor is one of the deadliest neurological diseases developed in the human brain. Gliomas are the most common type of brain tumor which are originated from the glial cells of the brain and are treated as the most treacherous tumors due to aggressiveness and heterogeneous structure. Early detection of gliomas increases the survival rate of the patients. In this paper, a hybrid deep neural network is proposed that uses inception v2 network hybridized with 16 new layered segmentation nets. The network is tested on BraTs 2020 and BraTs 2017 multi-parametric MRI (mPMRI) dataset to detect the whole tumor, and for the detection of tumor core (TC) and the edema, fast fuzzy C-means (FFCM) method is used. The proposed method attains an accuracy of 99.45% and a Dice similarity coefficient (DSC) of 89.74% for the whole tumor, an accuracy of 99.36%, a DSC of 86.47% for the tumor core and accuracy of 99.45%, and a DSC of 85.22% for the edema.

Keywords

• MPMRI
• Segmentation
• DNN
• FFCN
• Performance Parameters