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Brain tumour is one of the most dreadful kinds of tumour caused by the uncontrolled increase in the cells inside the skull. Clinically, brain tumour is diagnosed using different imaging methods, among which Magnetic Resonance Imaging (MRI) is the most extensively utilized technique. The proposed work intends to develop a Deep Learning (DL) based approach for segmenting and classifying to diagnose brain tumours from the brain MRI. This work proposes two novel approaches: (i) tumour segmentation technique using Conditional Random Field-Recurrent Neural Network (CRF-RNN), whose weights are adapted with the help of the novel Chronological Artificial Hummingbird Algorithm (CAHA). (ii) A brain tumour classification scheme using LeNet. Further, an optimization algorithm called Chronological Artificial Vultures Optimization (CAVO) is proposed for the weight optimization of the LeNet. It is observed that the devised model attained higher values of specificity, sensitivity, and accuracy of 0.938, 0.941, and 0.930.

the imaging science journal

Conditional random field-recurrent neural network segmentation with optimized deep learning for brain tumour classification using magnetic resonance imaging