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Brain tumor image classification is one of the predominant tasks of brain image processing. The three-class brain tumor classification becomes a trivial task for researchers as each tumor exhibit distinct characteristics. Existing classification models use deep neural networks and suffer from high computational cost. We have proposed an eight-layer average-pooling convolutional neural network to address three-class brain tumor classification. The proposed model uses three convolution blocks along with a dense layer and a softmax layer. We have utilized N-adam optimizer with a sparse-categorical crossentropy loss function to improve the learning rate. The proposed model has been evaluated using a dataset consists of 3064 brain tumor magnetic resonance images. The proposed model outperforms state-of-the-art models with 97.42% accuracy and takes lesser computation time than its competitive models.

Summary Brain cancer is one of the deadliest hazards in the world and hence tumor classification became a dominant task in brain tumor diagnosis. There is a wide range of brain tumors, and each tumor exhibits distinct properties like location, shape, size, and texture. Thus, multi‐class brain magnetic resonance (MR) image classification became a trivial task. In this article, we have proposed a seven‐layer convolutional neural network to address three‐class brain MR image classification. We have employed separable convolution to optimize computation time. The proposed separable convolution based neural network model exhibits accuracy of 97.52% on a publicly available dataset consists of 3064 images. The proposed model has analyzed with the help of four key parameters. Our proposed model exhibits superior performance than existing methods in key parameters. Further, our model takes less training time due to sparse network consists of seven layers.

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Brain tumour segmentation evolved as the dominant task in brain image processing. Most of the contemporary research proposals devise deep neural networks and sparse representation to address this issue. These methods inherently suffer from high computational cost and additional memory requirements. Thus, optimization of the computational cost became a challenging task for the contemporary research. This paper discusses an optimized U-Net model with post-processing for fast brain tumour segmentation. The proposed model includes two phases: training and testing. Training phase computes weights for optimized U-Net and an adaptive threshold value. In the testing phase, a trained U-Net model predicts a rough tumour segment. Adaptive thresholding grabs the final tumour with improved segmentation results. We have considered a brain tumour dataset of 3064 images with three types of brain tumours for evaluation. Our proposed model exhibits superior results than the existing models in terms of recall and dice similarity metrics. It exhibits competitive performance in accuracy and precision. Moreover, the proposed model outperforms its competitive models in training time.