使用深度神经网络进行疟疾细胞检测
Malaria Cell Detection Using Deep Neural Networks
AI总结：
本文基于ResNet50卷积神经网络和迁移学习，利用包含27,558张图像的Malaria Cell Images Dataset训练模型，实现疟疾感染细胞的自动检测，并开发了Streamlit Web应用以辅助诊断。
摘要：
疟疾仍然是全球最紧迫的公共卫生问题之一，造成显著的发病率和死亡率，尤其是在撒哈拉以南非洲地区。快速而准确的诊断对于有效治疗和疾病管理至关重要。传统的诊断方法，如血涂片的显微镜检查，劳动密集且需要大量专业知识，这在资源有限的环境中可能并不容易获得。本项目旨在利用深度学习方法实现疟疾感染细胞的自动化检测。我们采用了一种基于ResNet50架构的卷积神经网络（CNN），并利用迁移学习来提升性能。来自Kaggle的Malaria Cell Images Dataset包含27,558张图像，分为感染和未感染细胞两类，用于训练和评估。我们的模型表现出较高的准确率、精确率和召回率，表明其有潜力成为辅助疟疾诊断的可靠工具。此外，我们还使用Streamlit开发了一个Web应用程序，允许用户上传细胞图像并获取关于疟疾感染的预测结果，使该技术易于访问且用户友好。本文全面概述了方法、实验和结果，突出了深度学习在医学图像分析中的有效性。
英文摘要：
Malaria remains one of the most pressing public health concerns globally, causing significant morbidity and mortality, especially in sub-Saharan Africa. Rapid and accurate diagnosis is crucial for effective treatment and disease management. Traditional diagnostic methods, such as microscopic examination of blood smears, are labor-intensive and require significant expertise, which may not be readily available in resource-limited settings. This project aims to automate the detection of malaria-infected cells using a deep learning approach. We employed a convolutional neural network (CNN) based on the ResNet50 architecture, leveraging transfer learning to enhance performance. The Malaria Cell Images Dataset from Kaggle, containing 27,558 images categorized into infected and uninfected cells, was used for training and evaluation. Our model demonstrated high accuracy, precision, and recall, indicating its potential as a reliable tool for assisting in malaria diagnosis. Additionally, a web application was developed using Streamlit to allow users to upload cell images and receive predictions about malaria infection, making the technology accessible and user-friendly. This paper provides a comprehensive overview of the methodology, experiments, and results, highlighting the effectiveness of deep learning in medical image analysis.
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