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Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Computers, Materials & Continua 2023年第1期74卷 2179-2194页

作者： R.poonguzhali Sultan Ahmad p.thiruvannamalai sivasankar S.Anantha Babu pranav Joshi Gyanendra prasad Joshi Sung Won Kim Department of Computer Science and Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science and Engineering Jain Deemed to-be UniversityBangalore560069India Department of Neurology Annapurna Neuro HospitalKathmandu44600Nepal Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival *** location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and *** automatic recognition,place,and classifier process was desired and *** study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor *** presentedADRUSCM model majorly focuses on the segmentation and classification of *** accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in *** addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain ***,VGG-19 model is exploited as a feature ***,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes *** performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

关键词： Brain tumor diagnosis image classification biomedical images image segmentation deep learning

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