Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Center of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
Parasit Vectors. 2024 Apr 16;17(1):188. doi: 10.1186/s13071-024-06215-7.
Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model-but with improved accuracy-for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector.
The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3-C5) Res-block bodies of the backbone architecture's C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed.
The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone.
The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model's performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision.
疟疾是全球严重的公共卫生问题。早期和准确的诊断对于控制疾病的传播和避免严重的健康并发症至关重要。熟练技术人员对血涂片样本进行手动检查是传统疟疾诊断工具包中耗时的一个方面。疟疾在世界许多地方仍然存在，这强调了需要复杂和自动化的诊断仪器来加快感染细胞的识别，从而促进及时治疗并降低疾病传播的风险。本研究旨在引入一种更轻量级和更快的模型，但具有更高的准确性，用于使用 YOLOv4（你只看一次 v. 4）深度学习目标检测器诊断疟疾。
使用直接层修剪和骨干替换来修改 YOLOv4 模型。层修剪的主要目标是移除和单独分析骨干架构的 C3-C5 Res-block 体中的 C3、C4 和 C5（C3-C5）Res-block 体中的残差块。同时用较浅的 ResNet50 网络替换 CSP-DarkNet53 骨干以增强特征提取。比较和分析模型的性能指标。
修改后的模型优于原始 YOLOv4 模型。具有从 C3 和 C4 Res-block 体中修剪残差块的 YOLOv4-RC3_4 模型实现了最高的平均准确率精度（mAP）为 90.70%。这个 mAP 比原始模型高 9%以上，节省了大约 22%的十亿浮点运算（B-FLOPS）和 23 MB 的大小。研究结果表明，在从 CSP-DarkeNet53 骨干的 C3 Res-block 体中修剪冗余层后，YOLOv4-RC3_4 模型在检测感染细胞方面的表现也更好，检测精度提高了 9.27%。
本研究结果强调了使用 YOLOv4 模型检测感染的红细胞。从 Res-block 体中修剪残差块有助于确定哪些 Res-block 体分别对模型性能的贡献最大和最小。我们的方法有可能彻底改变疟疾诊断，并为基于深度学习的新型生物信息学解决方案铺平道路。开发一种有效和自动化的疟疾诊断方法将极大地促进全球对抗这种使人衰弱的疾病的努力。我们已经表明，去除不需要的残差块可以减小模型的大小和计算复杂度，而不会影响其精度。
