SqueezeNet-deep Kronecker net-based brain tumour classification using MRI image

Brain tumour diagnosis is a time-consuming process that heavily depends on the expertise and experience of radiologists. Thus, a SqueezeNet-Deep Kronecker Net (Squeeze-KNet) is devised for brain tumour classification. The proposed model provides an accurate diagnosis of brain tumours earlier. Initially, the pre-processing is done using an adaptive bilateral filter and then segmentation process is done using O-SegNet. Thereafter, the feature extraction process is done to extract the significant features like spider local image feature (SLIF), pyramid histogram of oriented gradients (PHOG), local vector pattern (LVP), Weber local binary pattern (WLBP), and local Gabor XOR patterns (LGXP). Finally, the brain tumour classification is done using the proposed Squeeze-KNet, which is the combination of deep Kronecker Net (DKN) and SqueezeNet. The proposed network is evaluated using accuracy, true positive rate (TPR), true negative rate (TNR), negative predictive value (NPV) and positive predictive value (PPV) and obtained 92.6%, 91.5%, 91.2%, 90.6%, and 90.1%.