Systems developed to work with computational intelligence have become very efficient, and in some cases obtain more accurate results than evaluations by humans. Hence, this project proposes a new online approach based on deep learning tools according to the concept of transfer learning to generate a computational intelligence framework for use with the Internet of Health Things (IoHT) devices. This framework allows the user to add their images and perform platform training almost as easily as creating folders and placing files in regular cloud storage services. The trials carried out with the tool showed that even people with no programming and image processing knowledge were able to set up projects in a few minutes. The proposed approach is validated using three medical databases, which include cerebral vascular accident images for stroke type classification, lung nodule images for malignant classification, and skin images for the classification of melanocytic lesions.