Combination of political optimizer, particle swarm optimizer, and convolutional neural network for brain tumor detection

•Automated brain tumor diagnosis from brain MRI.•Specific preprocessing for improving the system accuracy.•Using a new modified CNN for the final diagnosis.•Using an improved design of political optimizer for the CNN arrangement optimization. Manual detection of brain and tumor tissues takes a long time and is dependent on the state of the operator due to the great complexity of brain tissues. Experts are also required to study the images in order to discover these difficulties, rendering the traditional and outdated approaches ineffectual in their absence. As a result, using automated approaches for precise tumor examination will be quite beneficial. The use of magnetic resonance imaging technologies to diagnose brain cancers has garnered a lot of interest in recent years. One of the most generally utilized procedures in this field is magnetic resonance imaging, which has a great capability of revealing the interior structures of the human body. The present study uses an automated method to determine the tumorous cases from brain MRI. The images have been fed into an ideal convolutional neural network after being preprocessed. Here, a CNN optimized by a metaheuristic algorithm is used for providing a higher accuracy. The proposed CNN has been optimized by an improved version of political optimizer. The results are then compared with some other reported method to show its prominence toward the other methodologies.