Convolutional Neural Networks are usually the best choice for classification and semantic segmentation of images. categorical/numerical data (age, height, city, etc.) on the other hand are best processed by conventional machine learning models, such as (deep) Random Forests, Support Vector Machines or Conventional Neural Networks.
Are there hybrid architectures combining both convolutional and conventional Neural Networks for classification of datasets consisting of images and categorical data? I am sure this problem has been solved before and I am specifically looking for research papers, tutorials and implementations in one of the common libraries (PyTorch, Tensorflow, Keras, etc.).
A good example for such a "hybrid" dataset is the ISIC dataset, containing thousands of images of skin growths together with information such as the age and the sex of the patient for each image. Hypothetically, a hybrid model that includes this categorical/numerical information could be more succesful in detecting skin cancer, than a model which just uses the images.