Binary classification of similar images with small region of interest I have a dataset of microscope images and I want to train a ML/DL algorithm to perform binary classification. The positive class is when there is only one cell in the image, and the negative class is everything else (i.e. when there are either more than 1 cells, or no cells at all).

Below is one of the original images. (there is a cell in the curved site on the center of the image)



Due to the big size of the images (2048x2048) and the excess of information (the cells can only be in the tube-system), I decided to preprocess them. So, I set everything outside the tube-system to 0 (black) and I crop all the images to the boundaries I got by averaging the images of the whole dataset. Below you can see the end result. (there are 2 cells in the tube, one in the center and one at the upper left part)



Then I tried to train a CNN (using python and TensorFlow). I played a few times by changing its hyperparameters, but I had no luck. I think the problem is that the cells (region of interest) are occupying a very small portion of the image, which makes it hard for the algorithm to focus on. To make things worse, there are random dust particles around which make the image noisy.

Do you have any ideas of how I could perform a robust binary classification in such a dataset?
I would use a CLAHE preprocessing, SIFT Image features and mask the wrong detected keypoints out as geometric constrains. then i would count sift keypoints in a image without and keypoints to get a thredhold boundary and classify by amount of robust keypoints or logistic regression