Early Detection Of Canine Babesia From Red Blood Cell Images Using Deep Ensemble Learning - Boruah, Kuntala; Baruah, Dilip Kumar (Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2024)
Artificial intelligence-assisted medical diagnosis is enhancing accuracy with the contribution of several state of the art technologies such as Deep Learning (DL), Machine Learning (ML) and Image Processing (IP). From the detection of diseases to the selection of proper treatment plans, AI-powered assistance is effectively employed by healthcare professionals. Despite these advancements, the application of AI in animal healthcare is lagging behind, presenting a significant scope for AI adoption in veterinary medical diagnostics. This study addresses this gap by focusing on the automated diagnosis of canine Babesia infection, a parasitic disease that affects red blood cells (RBC). Our research contributed by developing a labeled dataset of microscopic images of red blood cells of infected and uninfected cases. During this work, four AI models are developed for automated classification: a custom Convolutional Neural Network (CNN), two pre-trained models (VGG16 ,DenseNet121) and a hybrid model (DenseNet121 + Support Vector Machine (SVM)). The performance of these models was 96.88%, 94%, 96.37% and 95.50% respectively. To further enhance the accuracy, a weighted average ensemble technique was employed. The ensemble model achieved an improved accuracy of 97.75%, demonstrating its potential. The enhanced performance of the ensemble model highlights the effectiveness of our method, significantly outperforming traditional methods and providing veterinarians with an efficient early diagnosis tool. This study is one of the few to address disease detection from microscopic images in animals using the potential of Artificial Intelligence.