Design a novel hybrid optimization with tuned deep convolutional neural network classifier for brain tumor segmentation and classification

Nowadays, segmentation and classification is most essential process to analyse the brain tumor disease. Moreover, Magnetic Resonance Imaging (MRI) scan images are used to helps the radiologist to diagnosis the tumor region using an efficient medical imaging techniques. In manual, it requires higher time to process each stage and image size is varied due to large amount of dataset. Several computer vision strategies are introduced in the literature for brain tumor segmentation and classification however, due to lower accuracy as well as ineffective decision making failed to provide the enhanced results. Therefore, this article develops the innovative hybrid Aquila coyote optimization algorithm is used to extract important elements that will be used in the classification process. Then, Deep Convolutional Neural Network (DCNN) classifier used as classification task while the weights are being modified and the suggested approach plays a significant role in improving the classification accuracy. With regard to the evaluation metrics, accuracy, sensitivity, and specificity, the suggested model's effectiveness is assessed. The performance is attained to be 97.3017%, 96.8194%, and 96.4079%, individually. This demonstrates how the suggested technique is better to those already in use for the efficient segmentation and categorization of brain tumours.