A deep learning based four-fold approach to classify brain MRI: BTSCNet

Incorrect diagnosis of brain tumor types prevent appropriate response to medical assistance and reduces patients' chances of survival. Examining MRI images of the patient's brain manually is one traditional method for distinguishing brain tumors, but it is time consuming and prone to human errors. Thus, an automated and new deep learning based four-fold method, Brain Tumor Segmentation and Classification Network (BTSCNet), is proposed in this article which will be helpful for physicians to classify three types of brain tumors (meningioma, glioma, and pituitary tumor) from T1-weighted contrast-enhanced MRI (CE-MRI) images properly. The proposed four folds includes: segmentation of brain tumor region using Brain Tumor Segmentation Network (BTSNet), ROI selection using morphological operation, feature extraction using multi-region gray level co-occurrence matrix (MR-GLCM), and classification using Sliding Window Euclidean distance (SWED) measure. The lack of annotated training samples is the main challenge in deep-learning-based brain MRI image classification. Thus, while training the proposed system, the scale, orientation and flip of the input image is randomly changed in the first fold of BTSCNet so that the network can be trained with varied or augmented images with respect to scale, orientation and flip. Four performance measures are used to measure the efficiency of the proposed method. The output of the proposed method is tested on a public database where the correct classification rates obtained are 96.6% (meningioma), 98.1% (glioma), and 95.3% (pituitary tumor), when considering MR-Contrast feature, MR-Correlation and MR-Homogeneity feature respectively, which indicates that the efficiency of the proposed method.