ObjectivesThe Three Delays Model has been commonly used to understand and prevent maternal mortality but has not been systematically applied to emergency medical conditions more generally. The objective of this study was to identify delays in emergency medical care seeking and delivery in rural Bangladesh and factors contributing to these delays by using the Three Delays Model as a framework.DesignA qualitative approach was used. Data were collected through focus group discussions and in-depth interviews using semistructured guides. Two analysts jointly developed a codebook iteratively and conducted a thematic analysis to triangulate results.SettingSix unions in Raiganj subdistrict of Bangladesh.ParticipantsEight focus group discussions with community members (n=59) and eight in-depth interviews with healthcare providers.ResultsDelays in the decision to seek care and timely receipt of care on reaching a health facility were most prominent. The main factors influencing care-seeking decisions included ability to recognise symptoms and decision-making power. Staff and resource shortages and lack of training contributed to delays in receiving care. Delay in reaching care was not perceived as a salient barrier. Both community members and healthcare providers expressed interest in receiving training to improve management of emergency conditions.ConclusionsThe Three Delays Model is a practical framework that can be useful for understanding barriers to emergency care and developing more tailored interventions. In rural Bangladesh, training community members and healthcare providers to recognise symptoms and manage acute conditions can reduce delays in care seeking and receiving adequate care at health facilities.

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This paper, deals with systematic study of simple segmentation and classification algorithms for kidney tumor using Computed Tomography images. Tumors are of different types having different characteristics and also have different treatment. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. This CT scans are visually examined by the physician for detection and diagnosis of kidney tumor. However this method lacks accuracy and detection of size of the tumor. So to overcome this, a computer aided segmentation technique has been proposed, which extracts the tumor part from the kidney, further on which feature extraction method is performed for extracting certain features and the type of tumor i.e. malignant or benign is displayed by using simple classifiers .

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