I was looking for another image dataset on Kaggle to continue to improve my Deep Learning knowledge and found images of Canine Coccidiosis.

The data included pictures of Canine Coccidiosis and labels highlighting the locations of the Coccidiosis. I found this dataset to be noticeably different from the Satellite image I previously reviewed due primarily to the lack of color differences between images. There area some tint color differences, but those differences are limited compared to the cloudiness that appeared in the satellite images.

My strategy for identify positive samples was to isolate 4 close up images for each Coccidiosis example from different centered offsets (Cyan Boxes). For negative samples, I ran an adaptive threshold on each image (Left Image) to highlight areas that appeared similar to Coccidiosis (Purple Boxes) .

I fit the samples into a Neural Network in TensorFlow. Then ran a screen over test images, with step size being half the size of the screen. My positive results (Green Box left image) are somewhat more refined than labeled data due to the rectangular data they give for a round protozoa. Bad guesses are represented as Red Boxes in the left screen, and Yellow in the right screen. Red boxes in the right screen are non-guesses, though are typically overlapping with positive guesses. My code on GitHub is here.