SSBTCNet: Semi-Supervised Brain Tumor Classification Network

Classification of brain tumors from the Magnetic Resonance Imaging (MRI) is a vital and challenging task for brain tumor diagnosis. Despite favorable results, from current Deep Learning (DL) methods used for the classification of brain tumors, the approaches are also used to cope with inconsistencies where the visual representation of non-brain tumor and brain tumor regions are equivalent. In this article, a novel semi-supervised DL-based approach is proposed for the classification of three types of brain tumors as well as no tumors. The proposed semi-supervised approach, Semi-Supervised Brain Tumor Classification Network (SSBTCNet), profits from the combination of an unsupervised AutoEncoder (AE) with supervised classification networks, in which an AE for learning concealed descriptors and a multi-layer perceptron-based classifier are trained concurrently. When compared to training the AE and classifier separately, the proposed semi-supervised learning fundamentally aids in tuning the learning of concealed descriptors for the objective of classification, resulting in improved brain MRI classification performance. To make SSBTCNet efficient, enhanced instances are generated by using a fuzzy-logic-based method to train and test the system. Also, a second approach, for the classification of the brain tumor as well as no tumor, with augmented unlabelled data is trained and tested which applies five types of augmentations to the images of the dataset to make the system robust from the change in different scales, flips and orientation. The output of the proposed method is tested on various datasets (with and without data augmentation as well as with and without labeled data) where the accuracy rate specifies that the proposed approach outperforms the other brain tumor classification approaches used in the literature.