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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.

Among the various systemic disorders that hands can suffer, rheumatoid arthritis is one of the most common. This disease affects the synovial tissue of the joints, making extension and flexion movements difficult. Currently, there are drugs on the market to treat it, but they produce dangerous side effects affecting the quality of life of the patient. In this context, physical rehabilitation plays an important role as a complement to the work of conventional medications. The present work describes the hand rehabilitation device that can be used to improve the mobility of people with rheumatoid arthritis. This glove shaped device can move all five fingers, using the measurement of the action potential of other healthy muscles. The data collection is done through a muscle sensor which processes the signal and sends the order to the glove, allowing contraction or relaxation of the hand. The glove was designed in 3D using the Fusion 360 software. In this case, this device captures the movements of patterns generated in a muscle and reproduces them in the fingers of the hand to increase the level of movement and prevent muscle atrophy in patients with this disease.