Abstract
The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.
Highlights
The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production
The hybrid platform of YOLOv2 with ResNet-50 detector helps improve the average precision of the proposed detector up to 81% compared to a previous single model[41]
The model-wise performance was assessed as to whether the classification model was the best-selected model based on an attention map and used to estimate P. gallinaceum-infected blood phases (Supplementary Fig. S1)
Summary
The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis, an AI subdivision, has been developed to identify human malaria infections and to classify their blood stage of the parasite growth. These can be used to assist clinical decision-making. Previous research has suggested a diagnostic tool in veterinary medicine focused on image analysis using machine learning techniques[17,18], such as microscopic examination used to help diagnose disorder and disease in fish farm[19]. Deep learning is a revolutionary and groundbreaking approach that has been incorporated recently into microscopic analysis for the veterinary medicine field. The work described above shows that deep learning algorithms can be applied successfully in the field of veterinary medicine
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