Advanced Detection and Categorization of Caprine Parasites: Using SSD

Abstract

Parasitic infections remain a significant global threat to animal and human health. Early and accurate diagnosis is crucial for effective intervention and management. This research explores the application of the Single Shot Multibox Detector (SSD) algorithm for rapid and precise detection and classification of diverse caprine parasites in fecal samples. A comprehensive dataset of microscopic images containing seven common caprine parasites named Amphistome, Ascaris, B-Coli, Moniezia, Schistosoma spindale, Strongyle, and Trichuris was established. The SSD model, specifically optimized for this task, achieved high performance, demonstrating its potential for rapid and accurate identification of caprine parasite infections. With single shot detection capability SSD algorithm can detect the parasites with high accuracy and speed. This makes it a valuable tool for improving diagnostic capabilities in caprine parasitology. Considering the zoonotic potential of parasitic infections, particularly in regions with close human-animal interactions, this research emphasizes the importance of utilizing advanced deep learning techniques like SSD to address this global challenge. The success of SSD in achieving precise and rapid categorizations paves the way for improved parasite diagnostics and ultimately, improved animal and human health outcomes.