000			02875nam a22002177a 4500
005			20250122091703.0
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040			_aAlliance University
082			_a006.3 WAN
100			_aWankhede, Disha Sushant
245			_aBrain Tumor Glioma Analysis Through Computational Intelligence
260			_aBengaluru _bAlliance University _c2024
300			_a168
502			_aAlliance University _bPh. D, _cAlliance College of Engineering Design, Alliance University, _d2024 _gGuide: Dr. Chetan Shelke _hAlliance College of Engineering Design
520			_aThe 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
650			_aComputer Science
650			_aComputer Science Interdisciplinary Applications
650			_aEngineering and Technology
856			_uhttps://shodhganga.inflibnet.ac.in/handle/10603/602803
942			_cBK
999			_c49056 _d49056