Classification of brain tumours from MRI images using deep learning-enabled hybrid optimization algorithm

Brain tumours are produced by the uncontrolled, and unusual tissue growth of brain. Because of the wide range of brain tumour locations, potential shapes, and image intensities, segmentation of the brain tumour by magnetic resonance imaging (MRI) is challenging. In this research, the deep learning (DL)-enabled brain tumour detection is developed by hybrid optimization method. The pre-processing stage used adaptive Wiener filter for minimizing the noise from input image. After that, the abnormal section of the image is segmented using U-Net. Afterwards, the data augmentation is accomplished to recover the random erasing, brightness, and translation characters. The statistical, shape, and texture features are extracted in feature extraction process. In first-level classification, the abnormal section of the image is sensed as brain tumour or not. Here, the Red Deer Tasmanian Devil Optimization (RDTDO) trained DenseNet is hired for brain tumour detection process. If tumour is identified, then second-level classification provides the brain tumour classification, where deep residual network (DRN)-enabled RDTDO is employed. Furthermore, the system performance is assessed by accuracy, true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with the maximum values of 0.947, 0.926, 0.950, 0.937, and 0.926 are attained.