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                <text>This study investigates the adoption and enhancement of digital health through e-consultation in Ghana's healthcare systems. It examines the challenges hindering the successful implementation of e-consultation, including security, privacy and awareness. By conducting a quantitative analysis and proposing a comprehensive model, this study aims to provide insights into overcoming these barriers, to improve healthcare accessibility and reduce disparities through e-consultation. The research integrates the DeLone and McLean IS Success Model, focusing on quality, use, user satisfaction, and net benefits, alongside the Diffusion of Innovation theory of technology adoption. The study employs a mixed-methods approach, combining literature review and quantitative analysis. Clinicians and patients received distinct questionnaires, covering knowledge, skills, performance, safety, and e-consultation challenges. Univariate statistics provides an initial data overview, followed by factor analysis. With a Kaiser-Meyer-Olkin value of 0.710 and Bartlett's test of sphericity yielding 1300.894, the data proves suitable for factor analysis. Using Principal Axis Factoring, the study reveals significant correlations among factors: Attitude, Regulatory Framework, Diffusion, and Acceptance. Based on these findings, an E-consultation Framework is proposed, emphasizing robust E-consultation Technology Frameworks. By adopting e-consultation and advanced digital health integration, Ghana can advance healthcare accessibility, diminish disparities, and enhance citizens' well-being within the digital health domain.</text>
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                <text>Automatic pancreas segmentation of CT scans enables physicians to identify and monitor the abnormalities in the pancreas. This facilitates intraoperative assistance, surgical planning, prognosis, and diagnosis. Nonetheless, the size and location of the pancreas in the CT image input data present a significant challenge for automatic segmentation, and the intricacy of the background region confounds deep segmentation networks. To solve this difficulty, we propose the Encoder-Corollary Atrous Spatial Pyramid Pooling-Decoder Network (EcD-Net) for locating and segmenting the pancreas. This two-tiered method begins with a coarse segmentation stage for locating the pancreas within the overall CT image. Using the detected image from the first tier, a fine segmentation network based on U-Net is applied to segment the target organ (pancreas). A novel Saturated Multi-view Dense Module (SMD- Module) is …</text>
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