Application-aided wound identification based on a multi-class classification convolutional neural network

Objective: this work proposed the development of an application for identifying types of wound tissue, through images, using a convolutional neural network trained with photographs. Method: study of technological production of the prototyping type. In the first phase, image preparation, training and evaluation of the convolutional neural network were carried out. The images were collected from a database of medical records of people treated at a reference wound treatment clinic, in a city in the north of Minas Gerais. In the second stage, an application was developed responsible for acquiring the image (photo) of the wound through the mobile device's camera, resizing and cropping the image and sending it to the cloud service so that it can predict the tissue type classification to the user. present in the wound. Results: the trained model obtained an accuracy of 76%, considered a good result and the application for classifying tissue types in a wound will be available in the Play Store and Apple virtual stores. Final Considerations: the mobility provided by the application will allow healthcare professionals to respond quickly when classifying a wound, as the indication of appropriate treatment and the direction towards complete resolution of the injury depend on this.