Deep Learning-Based HCNN and CRF-RRNN Model for Brain Tumor Segmentation

This paper proposes a strategy where a structure is developed to recognize and order the tumor type. Over a time of years, numerous specialists have been examined and proposed a technique in this space. A brain tumor segmentation approach is developed based on efficient, deep learning techniques implemented in a unified system to achieve the appearance and spatial accuracy outcomes through Conditional Radom Fields (CRF) and Heterogeneous Convolution Neural Networks (HCNN). In these steps the 2D image patching and picture slices of the deep-learning model is developed. The Proposed method has following steps as follows: 1) train HCNN by image patches; 2) train CRF with CRF-Recurrent Regression based Neural Network (RRNN) by means of image slices with fixed variables of HCNN; 3) fine tune with HCNN and CRF-RRNN image slices. In general, 3 segmentation models have been trained using axial-, coronary-and sagittal image patches and slices, Further assembled into brain tumor segments using a voting fusion technique and it can be examined with Internet of Medical Things (IoMT) Platform. The experimental results proved that our approach has been capable of developing a Flair, T1c and T2 segmenting model and of achieving good performance as with Flair, T1, T1c, and T2 scans.