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Intestinal parasitic infection leads to several morbidities to humansworldwide, especially in tropical countries. The traditional diagnosis usuallyrelies on manual analysis from microscopic images which is prone to human errordue to morphological similarity of different parasitic eggs and abundance ofimpurities in a sample. Many studies have developed automatic systems forparasite egg detection to reduce human workload. However, they work with highquality microscopes, which unfortunately remain unaffordable in some ruralareas. Our work thus exploits a benefit of a low-cost USB microscope. Thisinstrument however provides poor quality of images due to limitation ofmagnification (10x), causing difficulty in parasite detection and speciesclassification. In this paper, we propose a CNN-based technique using transferlearning strategy to enhance the efficiency of automatic parasiteclassification in poor-quality microscopic images. The patch-based techniquewith sliding window is employed to search for location of the eggs. Twonetworks, AlexNet and ResNet50, are examined with a trade-off betweenarchitecture size and classification performance. The results show that ourproposed framework outperforms the state-of-the-art object recognition methods.Our system combined with final decision from an expert may improve the realfaecal examination with low-cost microscopes.

Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning