BirCat Optimization for Automatic Segmentation of Brain Tumors and Pixel Change Detection Using Post-operative MRI Images

There is an emerging need for medical imaging data to provide patients with timely diagnosis. Magnetic resonance imaging (MRI) images based on brain tumor segmentation approaches possess greater importance in planning treatment. Though, mechanizing the process with different imaging conditions and accuracy is a major challenge due to variations in tumor structures. Hence, an efficient optimization-driven classifier, called BirCat optimization-based deep belief network (BirCat-based DBN) is developed to detect brain tumors. The introduced BirCat is devised by incorporating birdswarm algorithm (BSA) into cat swarm optimization (CSO) algorithm and is employed in tuning the DBN classifier. Here, the first step is pre-processing, where noises, as well as artifacts in input image, are eliminated by means of ROI extraction and filtering method. Then, for segmentation, region growing algorithm is used in which the distance is calculated by the modified Bhattacharya measure. Afterward, each segment is adapted for mining the segment-based features and pixel-based features used for classification. Then, the feature vector is formed and given to the DBN classifier, which is tuned with the help of the introduced BirCat for brain tumor detection. The introduced technique effectively determines the regions with the tumor in the input MRI image. Finally, the change detection is evaluated by analyzing the post-operative MRI image and the segmented image by means of pixel mapping strategy with respect to SURF features. The pixel mapping is utilized to evaluate the percentage change in tumor pixels. The proposed BirCat surpassed other prevailing approaches by producing maximal values of specificity, accuracy, sensitivity, F1-score, and Dice score at 0.92, 0.927, 0.938, 0.909, and 0.937, correspondingly, for dataset 2.