Investigación Científica Aplicada
Convolutional Neural Network for Detection and Localization of Intracranial Hemorrhages in Brain CT Images
Intracranial hemorrhage (ICH) is a life- threatening neurological condition that requires urgent and accurate diagnosis. Computed tomography (CT) is the prima-ry imaging modality used for early detection of ICH, yet manual interpretation of scans remains time-consuming and error-prone, this work proposes a deep learn-ing pipeline based on a U-Net architecture for automated detection and segmenta-tion of ICH in 2D axial brain CT images. The approach integrates a binary slice level classifier to identify potential hemor-rhagic slices, followed by a semantic segmentation to delineate hemorrhagic re-gions.
Descripcion completa
Intracranial hemorrhage (ICH) is a life- threatening neurological condition that requires urgent and accurate diagnosis. Computed tomography (CT) is the prima-ry imaging modality used for early detection of ICH, yet manual interpretation of scans remains time-consuming and error-prone, this work proposes a deep learn-ing pipeline based on a U-Net architecture for automated detection and segmenta-tion of ICH in 2D axial brain CT images. The approach integrates a binary slice level classifier to identify potential hemor-rhagic slices, followed by a semantic segmentation to delineate hemorrhagic re-gions.
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