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In the present work, we determined the necessary time to unchain the synthesis of vitamin D for a phototype II skin in Ibarra, Ecuador In a specific sky situation, the range of exposure time at Sun radiation is 4 to 6 minutes depending on the month of the year, but with clouds, it is needed about 70% more exposure time This time range is easily reached in normal conditions, but during a lockdown period, as in pandemic situations like the COVID 19 one or for people that need to live, work or study inside, if they are exposed to the direct Sun for the minutes indicated above, they will have a natural vitamin D supplement that will improve its health © New Testament Society of Southern Africa

Pigmented skin lesion identification is essential for detecting harmful pathologies related to this large organ, especially cancer. An analysis of the different methods and projects developed to diagnose these illnesses throughout the years showed that they had become very useful tools to identify melanoma, dermatofibroma, and basal cell carcinoma, among other types of cancer, are seen through the use of new computer-aided technologies. The most common diagnosis is based on dermoscopy and the dermatologist expertise that can improve accuracy with image detection techniques and classification by computer. Therefore, this study aims to develop software models able to detect and classify skin cancer. The following work is based on the use of dermoscopy images obtained from the HAM10000 dataset, a database with 10000 images previously tested and validated for research use. The main process is divided into three relevant parts: image segmentation, feature extraction (FE) using ten different pre-trained Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM) to establish a classification model. According to the results, the models of classification performed very well using the image segmentation step, showing average accuracies between 80.67% (Xception) and 90% (Alexnet). In contrast to the process without using image segmentation, where no method reached 60%. AlexNet plus SVM model showed the minor running time and presented the higher accuracy rate (90.34%) for the correct identification and classification of the seven categories of cutaneous lesions taken into account.