Brain Tumor Glioma Analysis Through Computational Intelligence

Alliance University Ph. D, Alliance College of Engineering Design, Alliance University, 2024 Guide: Dr. Chetan Shelke Alliance College of Engineering Design

The field of image processing offers distinctive features and is useful in medical diagnostics and imaging system. For the radiologists, manually identifying and classifying the Tumor has become a demanding and frantic process. Brain Magnetic resonance (MR) images must be extracted from malignant Tumor areas, which is a laborious and time-consuming task carried out by radiology experts or healthcare professionals. Current studies now heavily rely on medical imaging mainly to the continuous progress in automated brain Tumor classification and segmentation. This aids in quick decision as well as clear vision, diagnosis, and easier medication progression for the professionals. A dynamically Deep Learning technique for Glioblastoma brain cancer survival prediction rate was put out to address the aforementioned problems. newline In this research thesis, we present two approaches for detecting the brain tumor, risk prediction and measure the survival rate of patient. In the first approach, we developed the computer-aided tumor diagnosis techniques based on CNN that have demonstrated to be effective and have contributed considerable strides in computer vision. The deep learning method for predicting the prognosis of glioma brain tumors is covered in this research. Glioma prediction has been determined using MRI brain tumor imaging. Data pre-processing is the initial phase. The MRI brain images were improved by intensities normalization using histogram normalization, de-noising via bilateral filtering, and the removal of information contaminants. Probabilistic noise salted and peppers distortion was also taken out. Secondly, radiomic features segmentation was completed using the MFCM clustering approach. Then, Rough Set Theory-based Grey Wolf Optimization was used to choose the most important and instructive aspects from the obtained characteristics. Then, using FRCNN, the overall survival predictions categorization is performed to the important feature selection in MRI brain images. The proposed MFCM-RSGWO-FRCNN approach is te