Malaria is considered one of the deadliest diseases in today's world, which causes thousands of deaths per year. The parasites responsible for malaria are scientifically known as Plasmodium, which infects the red blood cells in human beings. Diagnosis of malaria requires identification and manual counting of parasitized cells in microscopic blood smears by medical practitioners. Its diagnostic accuracy is primarily affected by extensive scale screening due to the unavailability of resources. State of the art Computer-Aided Diagnostic techniques based on deep learning algorithms such as CNNs, which perform an end to end feature extraction and classification, have widely contributed to various image recognition tasks. In this paper, we evaluate the performance of Mosquito-Net, a custom made convnet to classify the infected and uninfected blood smears for malaria diagnosis. The CADx system can be deployed on IoT and mobile devices due to its fewer parameters and computation power, making it wildly preferable for diagnosis in remote and rural areas that lack medical facilities. Statistical analysis demonstrates that the proposed model achieves greater accuracy than the previous SOTA architectures for malaria diagnosis despite being 10 times lighter in parameters and inference time. Mosquito-Net achieves an AUC of 99.009% and an F-1 score of 96.7% on the validation set.

中文翻译：

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MOSQUITO-NET：基于深度学习的疟疾诊断 CADx 系统以及使用 GradCam 和类激活图的模型解释

疟疾被认为是当今世界上最致命的疾病之一，每年导致数千人死亡。导致疟疾的寄生虫在科学上被称为疟原虫，它会感染人类的​​红细胞。疟疾的诊断需要医生对显微镜血涂片中的寄生细胞进行识别和人工计数。由于资源不可用，其诊断准确性主要受到广泛规模筛选的影响。基于深度学习算法（如 CNN）的最先进的计算机辅助诊断技术，执行端到端的特征提取和分类，为各种图像识别任务做出了广泛贡献。在本文中，我们评估了 Mosquito-Net 的性能，这是一种定制的卷积网络，用于对感染和未感染的血液涂片进行分类以进行疟疾诊断。CADx 系统可以部署在物联网和移动设备上，因为它的参数和计算能力更少，使其非常适合在缺乏医疗设施的偏远和农村地区进行诊断。统计分析表明，尽管在参数和推理时间上轻 10 倍，但所提出的模型比以前的 SOTA 架构在疟疾诊断方面实现了更高的准确度。Mosquito-Net 在验证集上实现了 99.009% 的 AUC 和 96.7% 的 F-1 分数。