María Inés Susperreguy, Carolina Jiménez Lira, Chang Xu, Jo-Anne LeFevre, Humberto Blanco Vega, Elia Verónica Benavides Pando, Martha Ornelas Contreras
Home Learning Environments of Children in Mexico in Relation to Socioeconomic Status
Frontiers in psychology
recent years various datadriven algorithms applied classification staging brain glioma mri detection however restricted availability brain glioma mri data purely datadriven deep learning algorithms presented challenges extracting highquality features capturing complex patterns moreover analysis methods designed 2d data necessitate selection ideal tumor image slices align practical clinical scenarios research proposes novel brain glioma staging model medical cognition embedded mce model 3d data model embeds knowledge characteristics datadriven approaches enhance quality feature extraction approach includes following key components 1 deep feature extraction drawing upon imaging technical characteristics different mri sequences led design two methods algorithmic strategic levels mimic learning process real image interpretation medical professionals film reading 2 conduct extensive radiomics feature extraction capturing relevant features texture morphology grayscale distribution 3 referencing key points radiological diagnosis radiomics feature experimental results imaging characteristics various mri sequences manually create diagnostic features diagfeatures efficacy proposed methodology rigorously evaluated publicly available brats2018 brats2020 datasets comparing wellknown purely datadriven models method achieved higher accuracy recall precision reaching 9614 934 9706 9757 9280 9596 respectively