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Brain Tumor Segmentation through Level Based Learning Model

Computer Systems Science & Engineering 2023年第1期44卷 709-720页

作者： K.Dinesh Babu C.Senthil Singh Department of Electronics and Communication Engineering Adhi College of Engineering and Technology631605India Department of Electronics and Communication Engineering Infant Jesus College of EngineeringVallanaduThoothukudi628851India

Brain tumors are potentially fatal presence of cancer cells over a human brain,and they need to be segmented for accurate and reliable planning of *** process must be carried out in different regions based on which the stages of cancer can be accurately *** patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging(MRI)images possess varying sizes,shapes,positions,and *** scanner used for sensing the location of tumors cells will be sub-jected to additional protocols and measures for accuracy,in turn,increasing the time and affecting the performance of the entire *** this view,Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising *** previous strategies and models failed to adhere to diversity of sizes and shapes,proving to be a well-established solution for detecting tumors of bigger *** tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network(CNN).This proposal intends to furnish a detailed model for sensing early stages of cancer and hence perform segmentation irrespective of the current size and shape of *** size of networks and layers will lead to a significant weightage when multiple kernel sizes are involved,especially in multi-resolution *** the other hand,the proposed model is designed with a novel approach including a dilated convolution and level-based learning *** the convolution process is dilated,the process of feature extraction deals with multiscale objective and level-based learning eliminates the shortcoming of previous models,thereby enhancing the quality of smaller tumors cells and *** level-based learning approach also encapsulates the feature recon-struction processes which highlights the sensing of small-scale tumors ***,segmenting t

关键词： Glioma detection segmentation smaller tumour growth machine learning feature analysis

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