Using Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats

TL;DR

This paper demonstrates that convolutional neural networks can accurately determine reticulocyte percentages in cats from blood smear images, showing potential for accessible AI tools in veterinary diagnostics.

Contribution

The study introduces a practical CNN-based method for veterinary blood analysis, achieving high accuracy with minimal training data and emphasizing accessibility for veterinary professionals.

Findings

Achieved 98.7% accuracy in predicting reticulocyte count

Used only 800 labeled images for training

Showed deep learning can surpass human performance in this task

Abstract

Recent advances in artificial intelligence (AI), specifically in computer vision (CV) and deep learning (DL), have created opportunities for novel systems in many fields. In the last few years, deep learning applications have demonstrated impressive results not only in fields such as autonomous driving and robotics, but also in the field of medicine, where they have, in some cases, even exceeded human-level performance. However, despite the huge potential, adoption of deep learning-based methods is still slow in many areas, especially in veterinary medicine, where we haven't been able to find any research papers using modern convolutional neural networks (CNNs) in medical image processing. We believe that using deep learning-based medical imaging can enable more accurate, faster and less expensive diagnoses in veterinary medicine. In order to do so, however, these methods have to be accessible to everyone in this field, not just to computer scientists. To show the potential of this technology, we present results on a real-world task in veterinary medicine that is usually done manually: feline reticulocyte percentage. Using an open source Keras implementation of the Single-Shot MultiBox Detector (SSD) model architecture and training it on only 800 labeled images, we achieve an accuracy of 98.7% at predicting the correct number of aggregate reticulocytes in microscope images of cat blood smears. The main motivation behind this paper is to show not only that deep learning can approach or even exceed human-level performance on a task like this, but also that anyone in the field can implement it, even without a background in computer science.