Development and local validation of a three-dimensional U-Net deep learning model for intracranial haemorrhage segmentation on native CT scans in a resource-limited African setting

A CNN-Based AI Model for Detecting Acute Intracranial Hemorrhages on Non-contrast CT scans in Cohort of Blacks

Learning objectives Background Findings and procedure details Conclusion Personal information and co

Optimizing Liver Tumor Segmentation in CT Scans: A Genetic Algorithm Approach for Low-Resource Medical Imaging

Liver tumor segmentation is a crucial step in medical image analysis, aiding in the diagnosis and tr

RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES تقسيم المستوطنات الريفية على أساس التعلم العميق U - NET باستخدام صور الاستشعار عن بعد RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES SEGMENTATION DES RÈGLEMENTS RURAUX BASÉE SUR L'APPRENTISSAGE EN PROFONDEUR U-NET À L'AIDE D'IMAGES DE DÉTECTION À DISTANCE RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES

Abstract. Accurate and efficient extraction of rural settlements from high-resolution remote sensing

Development and validation of a predictive model for composite adverse outcomes in primary postpartum haemorrhage in a low-resource setting, Mpilo Central Hospital, Bulawayo, Zimbabwe.

Abstract Background Primary postpartum haemorrhage remains an important cause of maternal

SenTumorNet: A Lightweight 3D U-Net Model for Brain Tumor Segmentation in Sub-Saharan African MRI Data

Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans

Objective: Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebr