arXiv 2407.001202024-06-27cs.CVcs.AIcs.LGeess.IV

基于机器学习和深度学习技术的自动化网络疟疾检测系统

Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques

Addis Ababa Institute of Technology, Addis Ababa University（亚的斯亚贝巴大学亚的斯亚贝巴理工学院）；Opus College of Engineering, Marquette University（马凯特大学奥普斯工程学院）；Leuven Center for Affordable Healthcare Technology, KU Leuven（鲁汶大学鲁汶平价医疗技术中心）

Abraham G Taye, Sador Yemane, Eshetu Negash, Yared Minwuyelet, Moges Abebe, Melkamu Hunegnaw Asmare

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AI总结：

本研究提出基于深度学习和机器学习的自动化疟疾检测系统，利用CNN和迁移学习模型（VGG19、InceptionV3、Xception）及SVM，在NIH数据集上训练，最高准确率达97%，并通过网络界面供用户上传血涂片图像进行检测。

摘要：

疟疾寄生虫对全球健康造成重大负担，导致广泛的痛苦和死亡。准确检测疟疾感染对于有效治疗和控制至关重要。然而，现有的自动化检测技术在准确性和泛化能力方面存在局限性。许多研究关注特定特征，而未探索更全面的方法。在本研究中，我们提出了一种基于深度学习的疟疾感染细胞分类技术，使用传统CNN和迁移学习模型，特别是VGG19、InceptionV3和Xception。模型使用NIH数据集进行训练，并使用不同的性能指标进行测试，如准确率、精确率、召回率和F1分数。测试结果表明，深度CNN达到了最高准确率——97%，其次是Xception，准确率为95%。机器学习模型SVM达到了83%的准确率，而Inception-V3达到了94%的准确率。此外，该系统可通过网络界面访问，用户可上传血涂片图像进行疟疾检测。

英文摘要：

Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies have focused on specific features without exploring more comprehensive approaches. In our case, we formulate a deep learning technique for malaria-infected cell classification using traditional CNNs and transfer learning models notably VGG19, InceptionV3, and Xception. The models were trained using NIH datasets and tested using different performance metrics such as accuracy, precision, recall, and F1-score. The test results showed that deep CNNs achieved the highest accuracy -- 97%, followed by Xception with an accuracy of 95%. A machine learning model SVM achieved an accuracy of 83%, while an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can be accessed through a web interface, where users can upload blood smear images for malaria detection.

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