So I have a dataset of 6 different dermatology disease pictures along with data of the age, gender, etc other data for each picture.

I want to train a model that combines the image and the categorical/text data to classify it as one of the 6.

My initial thought is to use ResNet to get a 255x1 size vector for each image, then make 255 features with the value in each entry is the index in the vector. Then I can just add the 5 (let's say) features to get a final input size of 300x1 for each datapoint, which I can then train an LGBM classifier or any other classifier on. Does this sound like it would work?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.