Binary swallow swarm optimization with convolutional neural network brain tumor classifier for magnetic resonance imaging images

The brain tumor classification is implemented through biopsy, which is not normally executed before classic mind surgery. Machine learning (ML) algorithms assist radiologists in tumor analysis, not including obtrusive evaluations. The conventional ML strategies need separate feature extraction to tumor detect thus it needs more computation time to perform classification. Deep learning (DL) based convolution neural networks (CNNs) have been focused on brain tumor detection. In this paper, the CNN algorithm is improved based on meta-heuristics, which are used for pre-trained systems for databases to categorize MRI brain tumor images. Pre-trained DL, binary swallow swarm optimization (BSSO) is used for improving the weight and predispositions of the CNN algorithm. It is a block-wise calibrating system which is dependent on transfer learning. The current technique is assessed over a publically accessible magnetic resonance imaging (MRI) brain tumor database containing three categories as glioma, meningioma, and pituitary by the most noteworthy rate among everyone brain tumor in medical training. The proposed strategy is assessed over T1-weighted contrast-enhanced MRI (CE-MRI) benchmark data. To assess the execution, utilize the proposed strategies to the CE-MRI dataset for tumor detection and in the general execution of the BSSO-CNN model is estimated using the execution assessment measurements such as precision, sensitivity (recall), specificity, F1-score, and accuracy. Exploratory outcomes demonstrated with the purpose of the proposed strategy higher when compared to other methods to all metrics.