Brain Tumor Segmentation through Level Based Learning Model

作     者：K.Dinesh Babu C.Senthil Singh 

作者机构：Department of Electronics and Communication EngineeringAdhi College of Engineering and Technology631605India Department of Electronics and Communication EngineeringInfant Jesus College of EngineeringVallanaduThoothukudi628851India 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第1期

页      面：709-720页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：The authors received no specific funding for this study 

主　　题：Glioma detection segmentation smaller tumour growth machine learning feature analysis 

摘      要：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