En la actualidad, en donde se vive una crisis mundial de salud a causa de un virus llamado Covid-19, presentando un gran reto para el campo de la medicina y un cambio drástico en las vidas de las personas, se debe idear un modo diferente de poder llevar el duelo porque el virus sigue vigente en el entorno, además de haber perdido muchas vidas, no se permite realizar un duelo con el respeto y costumbre que se tiene en distintas culturas. A lo largo de la pandemia, se muestran momentos de angustia, dolor, depresión y caos; debiendo superar de manera drástica y resiliente. La presente investigación es de tipo mixta, exploratoria- descriptiva, cuyos resultados se realizan para el contexto académico mediante un muestreo aleatorio simple, en base al cálculo de la muestra y la predisposición de los encuestados de participar en forma voluntaria, teniendo como muestra a 261 personas entre jóvenes y adultos de 20 a más de 60 años. En conclusión, la resiliencia post pandemia se puede advertir en los encuestados que se encuentran proclives a un estado de depresión y ansiedad, el mismo que debe ser atendido por los especialistas brindando un apoyo psicosocial.

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

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In the present, where we live a pandemic because of Covid-19, it presents a challenge and change in the way we live for all, in which a different way of being able to receive health care must be created. in this research aimed to implement the electronic medical records system to improve patient care, such research is descriptive-explanatory in which a population of 67 patients from a health center is sampled. In conclusion, the implementation of the Electronic Medical Records System improved patient administrative care at the Health Center.

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