Multi-class brain tumor classification system in MRI images using cascades neural network

Abstract

Brain tumor segmentation from MRI is a challenging process that has positive ups and downs. The most crucial step for detection and treatment to save the patient's life is earlier diagnosis and classification of brain tumor (BT) with higher accuracy prediction. One of the deadliest cancers, malignant brain tumors is now the main cause of cancer-related death due to their extreme severity. To evaluate the tumors and help patients receive the appropriate treatment according to their classifications, it is essential to have a thorough understanding of brain diseases, such as classifying BT. In order to resolve the problem of low segmentation accuracy caused by an imbalance of model design and sample category in the process of brain tumor segmentation. In this research work, Multi-Dimensional Cascades Neural Network (MDCNet) is developed for multi-class BT classification. It is divided into two steps. In stage 1, an enhanced shallow-layer 3D locality net is used to conduct BT localization and rough segmentation on the preprocessed MRIs. It is also advised to use a unique circular inference module and parameter Dice loss to lower the uncertain probability and false positive border locations. In step 2, in order to compensate for mistakes and lost spatial information of a single view, morphological traits are investigated using a multi-view 2.5D net composed of three 2D refinement subnetworks. The suggested method outperforms the traditional model in segmentation, yielding an accuracy of 99.67%, 98.16%, and 99.76% for the three distinct datasets.