Tumour GAN-based augmentation and hybrid deep learning model for classification of brain tumour using MRI images

Most existing methods for BT classification are not accurate enough due to a lack of labelled data. Therefore, effective BT classification is essential for improving the survival rates and enhancing the overall well-being of patients. The major objective of this research is to introduce a hybrid network Spinal_LeNet for BT classification. Initially, the input magnetic resonance image (MRI) images undergo pre-processing. Subsequently, the image is segmented using squeeze M-SegNet. Thereafter, the image augmentation is done by the tumour generative adversarial network (TumourGAN). After the image augmentation, the extraction of features is employed to mine the significant morphological features like size and volume and histogram features such as magnitude, dispersion, asymmetry, flatness, and randomness. Finally, BT is classified using Spinal_LeNet, which is obtained by merging SpinalNet and LeNet. The devised model provides better values of positive predictive value of 90.03%, and sensitivity of 92.92% compared to the existing methods.