Detection and segmentation of meningioma tumors using improved cloud empowered visual geometry group (cloud-ivgg) deep learning structure

Detection and segmentation of meningioma brain tumor is a complex process due to its similar textural pattern with other tumors. In this paper Meningioma Tumor Detection System (MTDS) approach is proposed to detect and classify the meningioma brain images from the healthy brain images. The training work flow of the proposed MTDS approach consists of Spatial Gabor Transform (SGT), feature computations and deep learning structure. The features are computed from the meningioma brain image dataset images and the normal brain image dataset images and these features are fed into the classification architecture. In this paper, the proposed CLOUD-IVGG architecture is derived from the existing Cloud empowered Visual Geometry Group (VGG) architecture to improve the detection rate of the proposed system and to decrease the computational time complexity. The testing work flow of the proposed system is also consist of SGT, feature computation and the CLOUD-IVGG architecture to produce the classification result of the source brain images into either normal or meningioma. Further, the tumor regions in this meningioma image have been located using the Morphological segmentation algorithm. In this research work, two independent resource brain imaging datasets has been involved to estimate and validate the performance efficiency of the proposed MTDS. The datasets are Kaggle Brain Imaging (KBI) and BRATS Imaging 2020 (BI20). The performance efficiency has been analyzed with respect to detection rate, precision, recall and Jaccard index