Deep Learning for Clinical Image Classification of Genital Lesions Caused by Sexually Transmitted Diseases

Sexually transmitted diseases (STDs) are one of the world’s major health emergencies. Given its incidence and prevalence, particularly in developing countries, it is necessary to find new methods for early diagnosis and treatment. However, this can be complicated in geographical areas where medical care is limited. In this article, we present the basis of a deep learning-based system for image classification of genital lesions caused by these diseases, built using a convolutional neural network model and methods such as transfer learning and data augmentation. In addition, an explainability method (GradCam) is employed to enhance the interpretability of the obtained results. Finally, we developed a web framework to facilitate additional data collection and annotation. This work aims to be a starting point, a “proof of concept” to test various different approaches, for the development of more robust and trustworthy Artificial Intelligence approaches for medical care in STDs, which could substantially improve medical assistance in the near future, particularly in developing regions.