Comparative Study of CNN-ML Models for Detection and Classification of Canine Babesia in Blood Cell Image

Veterinary diagnostics is a field that demands AI-based solutions suitable for resource constraint environment. Traditional animal disease detection methods have significant scope for AI adoption to automate certain aspects of the diagnosis process. In this study, twelve CNN-ML hybrid models are developed, leveraging the power of Convolutional Neural Networks (CNN) combined with traditional machine learning approaches. Pre-trained models are employed for feature extraction paired with traditional ML classifiers such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boost (XGBoost) and AdaBoost. These twelve hybrid models were categorized based on their CNNbackbone: category I (DenseNet121+ML models), category II (VGG16+ML models) and category III(InceptionV3+ML models). The top-performing hybrid models from each category i.e., DenseNet121+RF (96.00% accuracy) from category I, VGG16+SVM (93.5% accuracy) from category II and InceptionV3+SVM (92.60% accuracy) from category III are selected for ensemble modeling. The weighted ensemble achieved an accuracy of 95.5%, while the voting ensemble is able to achieve the accuracy to 96.62%. The ensemble model is shown to be more generalized and robust compared to the individual hybrid models. Additionally, as training time was a crucial consideration in resource-constrained environment, we concluded that weighted ensemble model is efficient as its training time is only 0.97 seconds.