Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India.
Department of CSE, Swami Vivekanand Institute of Engineering and Technology, Ramnagar, India.
Sci Rep. 2024 Mar 8;14(1):5753. doi: 10.1038/s41598-024-56323-8.
Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.
寄生虫是一个主要的全球健康威胁，主要在缺乏先进医疗设施的地区。早期和准确地检测寄生虫对于拯救生命至关重要。深度学习模型通过在诊断、检测和分类疾病方面提供有希望的结果，提升了医疗领域。本文探讨了深度学习技术在检测和分类各种寄生虫中的作用。该研究工作基于一个数据集，其中包含 34298 个寄生虫样本，如刚地弓形虫、锥虫、疟原虫、利什曼原虫、巴贝虫和滴虫，以及红细胞和白细胞等宿主细胞。这些图像首先从 RGB 转换为灰度，然后计算周长、高度、面积和宽度等形态特征。之后，应用 Otsu 阈值和分水岭技术将前景与背景区分开来，并在图像上创建标记，以识别感兴趣的区域。使用 VGG19、InceptionV3、ResNet50V2、ResNet152V2、EfficientNetB3、EfficientNetB0、MobileNetV2、Xception、DenseNet169 和一种混合模型 InceptionResNetV2 等深度迁移学习模型。使用三种优化器（SGD、RMSprop 和 Adam）对这些模型的参数进行微调。实验结果表明，当应用 RMSprop 时，VGG19、InceptionV3 和 EfficientNetB0 达到了 99.1%的最高准确率，损失为 0.09。同样，使用 SGD 优化器，InceptionV3 表现出色，达到了 99.91%的最高准确率，损失为 0.98。最后，应用 Adam 优化器时，InceptionResNetV2 表现出色，达到了 99.96%的最高准确率，损失为 0.13，优于其他优化器。本研究的结果表明，结合图像处理方法使用深度学习模型可以产生一种高度准确和高效的寄生虫检测和分类方法。
