 Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion NetworkP. Sobana Sumi and Radhakrishnan Delhibabu () MPRA Paper from University Library of Munich, Germany Abstract: Brain tumor is a growth of abnormal cells in brain, which canbe cancerous or non-cancerous. The Brain tumor have scarce symptomsso it is very difficult to classify. Diagnosing brain tumor with histologyimages will efficiently helps us to classify brain tumor types. Sometimes,histology based image analysis is not accepted due to its variations inmorphological features. Deep learning CNN models helps to overcomethis problem by feature extraction and classification. Here proposed amethod to classify high resolution histology image. InceptionResNetV2is an CNN model, which is adopted to extract hierarchical features with-out any loss of data. Next generated deep spatial fusion network to ex-tract spatial features found in between patches and to predict correct fea-tures from unpredictable discriminative features. 10-fold cross-validationis performed on the histology image. This achieves 95.6 percent accu-racy on 4-class classification (benign, malignant, Glioblastoma, Oligo-dendroglioma). Also obtained 99.1 percent accuracy and 99.6 percentAUC on 2-way classification (necrosis and non-necrosis). Keywords: Glioblastoma Multiforme; Deep spatial fusion network; InceptionResNetV2; classification; patches; CNN (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:97315 Access Statistics for this paper More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.