Telemedicine and e-health in Algeria facing challenges in medical practice

The Algerian health system operates in a demographic, economic, environmental, and societal specific context to meet the challenges of epidemiological transition. Development of information and communication technologies in this atmosphere allow telemedicine and e-health emergence in the vast country of Algeria. Thus, the country may face these challenges by including them in a national telemedicine plan and making a major focus of global action for the prevention and control of prevalent diseases. The interest to adopt this tool in daily practice stems from an improvement in the quality of communication between practitioners and in the doctor–patient relationship with a possibility of quick access to care and more efficient care pathways. Thus, national goals in fight plans against diseases will be achieved. Telemedicine and e-health projects that methodologically well-defined, respecting regulations and using all means and all available resources, including WHO mobile health, are to be designed and implemented in all areas, especially in the national plan against not-communicated diseases, maternal and child health, old aging health, and mental health. This approach will integrate telemedicine in the health care system whose inevitable implementation can be done on solid foundations and will be actively supported by SATeS

Efficient embedding network for 3D brain tumor segmentation

International audience3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and trained. In this paper, we investigate a way to transfer the performance of a two-dimensional classification network for the purpose of three-dimensional semantic segmentation of brain tumors. We propose an asymmetric U-Net network by incorporating the EfficientNet model as part of the encoding branch. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance