94 Smartphone integration in anemia detection for small ruminants with Convolutional Neural Networks

Abstract Anemia, often caused by internal parasites like Haemonchus contortus, presents significant health and productivity challenges for small ruminants. The primary goal of this study was to accurately distinguish between healthy and anemic goats using an image classification system focused on eye conjunctiva images. Over 5,000 eye conjunctiva images from 75 goats were collected at Fort Valley State University farms over a 3-mo period using smartphone cameras. These images were randomly divided into training (70%) and testing (30%) datasets, with each group containing five subfolders corresponding to FAMACHA scores of 1, 2, 3, 4, and 5. A Convolutional Neural Network (CNN) algorithm was utilized for image analysis, incorporating data augmentation techniques such as Resize, RandomHorizontalFlip, RandomVerticalFlip, and RandomRotation. The CNN model was built on the Google Colaboratory platform using CUDA 11.2 and the PyTorch machine learning framework, incorporating three ConvNet layers. The model training used the Adam Optimizer with a slower learning rate of 0.001 and a weight decay of 0.0001 to prevent exploding gradient issues, alongside ReLU and the cross-entropy loss function over 1000 epochs. Results demonstrate that the CNN model was highly effective in classifying eye conjunctiva images of goats to detect anemia based on FAMACHA scores.