Leveraging MobileNetV2 and deep learning innovation for high accuracy Plasmodium Vivax detection in blood smears.

IF 3.4 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Vivek Morris Prathap, Sonam Yadav, Tabish Qidwai
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引用次数: 0

Abstract

Malaria remains a significant public health challenge in regions where it is endemic. Pregnant women and young children are particularly vulnerable to the disease. Effective and timely diagnostic methods are crucial for reducing severe health outcomes. These methods help prevent deaths and lessen the widespread clinical and epidemiological burden on at-risk populations. However, traditional methods involved in the process of malaria parasite detections such as microscopic examination of blood smear by medical trained technicians is known to be time consuming, purely subjective and highly prone to errors. Therefore, Artificial Intelligence (AI) based OD (Object Detection) model like YOLO are preferred for overcoming these issues faced by traditional approaches as YOLO is known to be more rapid and precise by predicting bounding boxes and class probabilities than other models. However, existing YOLO model face challenges such as higher localization error, struggle with small objects and better accuracy of the model. Therefore, proposed research work focuses on employing YOLOv3 model with modified MobileNetv2 in backbone structure for classifying Plasmodium vivax (P. vivax) cells with the aim of improving the performance and speed of the model for detecting objects as MobileNetv2 is known for its faster processing and reduced resource consumption. However, accuracy is still measured as one of the key downsides for detecting and classifying the classes of thin blood smear, therefore modified MobileNetv2 is used, where proposed TCL (Transformed Convolution Layer) is employed at bottleneck layer, where weights are calculated based on different classes of image features thereby making the process more effective for classifying the infected and uninfected malaria cells of thin blood smear images effective. Besides, the performance of the proposed model is evaluated by implementing different metrics where the findings obtained are accuracy value of 1.00, precision value of 0.98, recall of 0.98, F1 score of 0.97 and mean average precision (mAP) value of 0.90. The major contribution of the study focuses on providing a better diagnostic approach for medical professionals in order to obtain improved results.

利用MobileNetV2和深度学习创新,在血液涂片中进行高精度间日疟原虫检测。
在疟疾流行的地区,疟疾仍然是一项重大的公共卫生挑战。孕妇和幼儿特别容易感染这种疾病。有效和及时的诊断方法对于减少严重的健康后果至关重要。这些方法有助于预防死亡,减轻危险人群普遍面临的临床和流行病学负担。然而,传统的疟疾寄生虫检测方法,如由受过医学训练的技术人员对血液涂片进行显微镜检查,众所周知是耗时的,纯粹主观的,而且很容易出错。因此,像YOLO这样基于人工智能(AI)的OD(目标检测)模型是克服传统方法面临的这些问题的首选,因为YOLO通过预测边界框和类别概率比其他模型更快、更精确。然而,现有的YOLO模型面临着定位误差大、目标小、模型精度低等问题。因此,我们建议的研究工作重点是利用改进的MobileNetv2骨架结构的YOLOv3模型对间日疟原虫(P. vivax)细胞进行分类,以提高模型检测目标的性能和速度,因为MobileNetv2具有更快的处理速度和更少的资源消耗。然而,准确性仍然是检测和分类薄血涂片类别的关键缺点之一,因此使用改进的MobileNetv2,在瓶颈层采用提出的TCL (Transformed Convolution Layer),根据不同类别的图像特征计算权值,从而使该过程更有效地对薄血涂片图像的感染和未感染疟疾细胞进行分类。此外,通过实施不同的指标来评估模型的性能,得到的结果是准确率值为1.00,精度值为0.98,召回率为0.98,F1得分为0.97,平均平均精度(mAP)值为0.90。这项研究的主要贡献在于为医疗专业人员提供更好的诊断方法,以获得更好的结果。
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来源期刊
Saudi Pharmaceutical Journal
Saudi Pharmaceutical Journal PHARMACOLOGY & PHARMACY-
CiteScore
6.10
自引率
2.40%
发文量
194
审稿时长
67 days
期刊介绍: The Saudi Pharmaceutical Journal (SPJ) is the official journal of the Saudi Pharmaceutical Society (SPS) publishing high quality clinically oriented submissions which encompass the various disciplines of pharmaceutical sciences and related subjects. SPJ publishes 8 issues per year by the Saudi Pharmaceutical Society, with the cooperation of the College of Pharmacy, King Saud University.
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