AI-based Cattle Disease Detection System Using Modified CNN Architecture for Blood Smear Image Analysis - Sravani P; Mariya Sneha T; Shiva Sumanth Reddy, (Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080), 2026)
Tick-borne diseases such as babesiosis and anaplasmosis pose significant threats to cattle health, causing substantial economic losses in livestock farming. Traditional microscopic diagnosis of these diseases through blood smear analysis is time-consuming, labor-intensive, and prone to human error. This study presents an AI-based cattle disease detection system utilizing a modified convolutional neural network (CNN) architecture for automated blood smear image analysis. The proposed system implements a three-tier architecture comprising farmer, lab technician, and veterinary doctor modules, enabling seamless coordination in disease diagnosis. This modified CNN architecture incorporates optimized convolutional layers with ReLU activation, max-pooling strategies, and dropout regularization to enhance feature extraction from microscopic blood smear images. The system achieved 98.0% accuracy for babesiosis detection and 97.7% accuracy for anaplasmosis detection, with precision and recall exceeding 95%. Experimental results demonstrate that the proposed approach significantly outperforms traditional diagnostic methods and baseline CNN models, providing rapid, accurate, and cost-effective disease detection for improved cattle health management.