Nakasi Rose, Nabende Joyce Nakatumba, Tusubira Jeremy Francis, Bamundaga Aloyzius Lubowa, Andama Alfred
Makerere University, P.O Box 7062, Kampala, Uganda.
Mulago National Referral Hospital, P.O Box 7051, Kampala, Uganda.
Data Brief. 2024 Dec 2;58:111190. doi: 10.1016/j.dib.2024.111190. eCollection 2025 Feb.
Malaria is a major public health challenge in sub-Saharan Africa. Timely and accurate diagnosis of malaria is vital to reduce the caseload and mortality rates associated with malaria The use of microscopy in malaria screening is the gold standard recommended method by the World Health Organisation (WHO). In Uganda, utilization of microscopy is challenged by insufficient expertise to interpret the images accurately, affecting the efficiency, effectiveness and accuracy of malaria detection and diagnosis. We present a benchmark dataset of thick and thin blood smear images for automatic malaria screening in Uganda. Mobile Microscopy data was collected from Mulago Hospital, Department of Internal Medicine, Makerere University and Kiruddu National Referral Hospital in Uganda. The labelled image data can be used to build computational models implemented with convolution neural networks. The dataset has 3000 labelled thick blood smear images and 1000 labelled thin blood smear images. The datasets will support robust and accurate deep learning models for malaria diagnosis using thick and thin blood smear images with reasonable detection accuracies.
疟疾是撒哈拉以南非洲地区面临的一项重大公共卫生挑战。及时、准确地诊断疟疾对于减少疟疾病例数和死亡率至关重要。在疟疾筛查中使用显微镜检查是世界卫生组织（WHO）推荐的金标准方法。在乌干达，显微镜检查的应用受到准确解读图像的专业知识不足的挑战，这影响了疟疾检测和诊断的效率、有效性和准确性。我们提供了一个用于乌干达自动疟疾筛查的厚血涂片和薄血涂片图像基准数据集。移动显微镜数据是从乌干达穆拉戈医院内科、马凯雷雷大学以及基鲁杜国家转诊医院收集的。带标签的图像数据可用于构建使用卷积神经网络实现的计算模型。该数据集有3000张带标签的厚血涂片图像和1000张带标签的薄血涂片图像。这些数据集将支持使用厚血涂片和薄血涂片图像、具有合理检测准确率的强大而准确的深度学习模型用于疟疾诊断。
