Intestinal parasitic infections (IPIs), caused by protozoan and helminths parasites, are among the most common infections in humans in low- and middle-income countries. These infections affect approximately 3.5 billion people, with the majority being children. According to the WHO, about one-fourth of the global population is infected with intestinal parasites, primarily Ascaris lumbricoides, Trichuris trichiura, and hookworms. Furthermore, soil-transmitted helminths (STH) affect around 2 billion people worldwide. Parasitic infections are regarded as a severe public health concern, as they cause a wide array of possibly detrimental symptoms ranging from iron deficiency, growth retardation, asymptomatic carriage to diarrhea, abdominal pain, general malaise, weakness, and other physical and mental health problems. Early diagnosis represents the crucial weapon in the fight against parasitic infections. Existing methods to detect (localize and classify) parasitic egg are not accurate. These methods sometimes lose attention on objects in the images which leads to misclassification and low accuracy. This invention proposes to train state-of-the-art object detection method of EfficientDet to localize parasitic eggs and categories them into eleven classes.