I have a normal/tumor medical images dataset and, for the same patients, also the relative genomics, and my goal is to predict if a patient has a tumor by combining all the information.
To achieve this, I am using a ResNet50 with imagenet weights to extract features from images, and other methods to extract features from genes. I join the two features and use an SVM to make a prediction.
The accuracy isn't extremely bad, but I wanted to know if it can be increased by performing a fine-tuning over the same images, in place of using a network only trained on the imagenet datasets as it is.
I have researched papers but I found literally nothing, neither in favor nor in contrast with this hypothesis.
Is there some known contraindication?