使用改进的 YOLOV3 和 YOLOV4 模型检测厚血涂片显微镜图像中的疟原虫。

BACKGROUND: Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the "gold standard" for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists' diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. RESULTS: YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. CONCLUSIONS: The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

背景：使用 Leishman/Giemsa 染色的薄血涂片和厚血涂片进行手动显微镜检查仍然是疟疾诊断的“金标准”。这种方法的一个缺点是，其准确性、一致性和诊断速度取决于显微镜检查者的诊断和技术技能。在发展中国家的偏远地区，很难获得技术熟练的显微镜检查者。为了解决这个问题，在本文中，我们提出研究用于从厚血涂片的显微镜图像中自动筛选疟原虫的一阶段和两阶段目标检测算法的最新进展。

结果：YOLOV3 和 YOLOV4 模型是准确性和速度方面的最新目标检测算法，但它们没有针对在显微镜图像中检测小目标（如疟原虫）进行优化。我们通过增加特征尺度和添加更多检测层来修改这些模型，以在不显著降低检测速度的情况下增强检测小目标的能力。我们提出了一个修改后的 YOLOV4 模型，称为 YOLOV4-MOD，以及两个修改后的 YOLOV3 模型，分别称为 YOLOV3-MOD1 和 YOLOV3-MOD2。此外，使用 K-means 聚类算法生成新的锚框大小，以利用这些模型在小目标检测方面的潜力。修改后的 YOLOV3 和 YOLOV4 模型在公开的疟疾数据集上进行了评估。这些模型在平均精度 (mAP)、召回率、精度、F1 分数和平均 IOU 方面超过了其原始版本、Faster R-CNN 和 SSD 的性能，实现了最先进的精度。YOLOV4-MOD 在所有其他模型中取得了最佳的检测准确性，mAP 为 96.32%。YOLOV3-MOD2 和 YOLOV3-MOD1 的 mAP 分别为 96.14%和 95.46%。

结论：本研究的实验结果表明，修改后的 YOLOV3 和 YOLOV4 模型的性能非常有希望用于从智能手机摄像头在显微镜目镜上拍摄的图像中检测疟原虫。所提出的系统适用于部署在资源有限的地区。