Detection and Segmentation of Glioma Tumors Using an Improved Visual Geometry Group (IVGG) Deep Learning Structure

Abstract

Abstract Glioma brain tumors have similar textural patterns to other tumors, making their detection and segmentation a challenging process. The approach of the Modified Tumor Detection System (MTDS) is presented in this study to identify and categorize brain images of gliomas from images of healthy brains. The Spatial Gabor Transform (SGT), feature calculations, and deep learning structure comprise the training work flow of the suggested MTDS technique. The features are computed from the glioma 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 IVGG architecture is derived from the existing 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 IVGG architecture to produce the classification result of the source brain images into either normal or glioma. Furthermore, the Morphological Segmentation technique has been used to find the tumor locations in this glioma image. Two separate brain imaging datasets have been used in this study to evaluate and validate the suggested MTDS's performance efficiency. BRATS Imaging 2020 (BI20) and Kaggle Brain Imaging (KBI) are the datasets. Analysis of the performance efficiency has been done in relation to the Jaccard index, recall, precision, and detection rate.

Keywords

• MTDS
• glioma
• brain tumors
• deep learning
• IVGG