Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model  

作　　者：Amarendra Reddy Panyala M.Baskar 

机构地区：[1]Department of Computer Science and Engineering,School of Computing,SRM Institute of Science and Technology,Kattankulathur,Chengalpattu,Chennai,603203,Tamilnadu,India [2]Department of Information Technology,MLR Institute of Technology,Hyderabad,500043,Telangana,India [3]Department of Computing Technologies,School of Computing,SRM Institute of Science and Technology,Kattankulathur,Chengalpattu,Chennai,603203,Tamilnadu,India

出　　处：《Computer Systems Science & Engineering》2023年第9期3883-3899,共17页计算机系统科学与工程（英文）

摘　　要：The deep learning models are identified as having a significant impact on various problems.The same can be adapted to the problem of brain tumor classification.However,several deep learning models are presented earlier,but they need better classification accuracy.An efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this issue.The method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple levels.The noise-removed image has been equalized for its quality by using histogram equalization.Further,the features like white mass,grey mass,texture,and shape are extracted from the images.Extracted features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality reduction.The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution layer.The neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images.Based on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关 键 词：CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture GABOR 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]