I am working on image classification on a really small dataset which contains around 220 images with two labels (110 images for each label). My goal is to visualize the common feature extracted among each of the classes/labels to understand why certain images are classified as the first label instead of the second one. The images are labeled by experiment and we are trying to understand why they are labeled in that way by visualizing the feature.
What would be a good approach for this problem? I've considered deep learning but I think the dataset might be too small for it. And also what would be a proper practice to visualize the features?