Automatic classification of Giardia infection from stool microscopic images using deep neural networks.

Introduction: Giardiasis is a common intestinal infection caused by the Giardia lamblia parasite, and its rapid and accurate diagnosis is crucial for effective treatment. The automatic classification of Giardia infection from stool microscopic images plays a vital role in this diagnosis process. In this study, we applied deep learning-based models to automatically classify stool microscopic images into three categories, namely, normal, cyst, and trophozoite.

Methods: Unlike previous studies focused on images acquired from drinking water samples, we specifically targeted stool samples. In this regard, we collected a dataset of 1610 microscopic digital images captured by a smartphone with a resolution of 2340 × 1080 pixels from stool samples under the Nikon YS100 microscope. First, we applied CLAHE (Contrast Limited Adaptive Histogram Equalization) histogram equalization a method to enhance the image quality and contrast. We employed three deep learning models, namely Xception, ResNet-50, and EfficientNet-B0, to evaluate their classification performance. Each model was trained on the dataset of microscopic images and fine-tuned using transfer learning techniques.

Results: Among the three deep learning models, EfficientNet-B0 demonstrated superior performance in classifying Giardia lamblia parasites from stool microscopic images. The model achieved precision, accuracy, recall, specificity, and F1-score values of 0.9599, 0.9629, 0.9619, 0.9821, and 0.9607, respectively.

Conclusion: The EfficientNet-B0 showed promising results in accurately identifying normal, cyst, and trophozoite forms of Giardia lamblia parasites. This automated classification approach can provide valuable assistance to laboratory science experts and parasitologists in the rapid and accurate diagnosis of giardiasis, ultimately improving patient care and treatment outcomes.