基于轻量级深度卷积神经网络的疟疾诊断

IF 3.1 Q2 HEALTH CARE SCIENCES & SERVICES
Varun Magotra, Mukesh Kumar Rohil
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引用次数: 8

摘要

人工智能在医疗保健领域的应用日益增多。卷积神经网络(CNN)和基于掩模区域的CNN (Mask-RCCN)在医学领域的应用给医学图像分析带来了革命性的变化。cnn主要用于识别、分类和特征提取任务,并且在这些任务中表现出色。在我们的研究中,我们提出了一种轻量级的CNN,它需要更少的训练时间,用于识别疟疾寄生红细胞并将其与健康红细胞区分开来。为了比较我们模型的准确性,我们在两个模型上使用迁移学习,即VGG-19和Inception v3。我们在三种不同的配置中训练我们的模型,这取决于输入模型进行训练的数据的比例。对于这三种配置,我们提出的模型能够达到96%左右的精度,这比我们为相同的三种配置训练的其他模型都要高。结果表明,该模型在较低的计算需求下具有较好的性能。因此,它可以更有效地使用,并且可以很容易地用于检测疟疾细胞。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network
The applications of AI in the healthcare sector are increasing day by day. The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classification, and feature extraction tasks, and they have delivered a great performance at these tasks. In our study, we propose a lightweight CNN, which requires less time to train, for identifying malaria parasitic red blood cells and distinguishing them from healthy red blood cells. To compare the accuracy of our model, we used transfer learning on two models, namely, the VGG-19 and the Inception v3. We train our model in three different configurations depending on the proportion of data being fed to the model for training. For all three configurations, our proposed model is able to achieve an accuracy of around 96%, which is higher than both the other models that we trained for the same three configurations. It shows that our model is able to perform better along with low computational requirements. Therefore, it can be used more efficiently and can be easily deployed for detecting malaria cells.
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来源期刊
CiteScore
6.90
自引率
2.30%
发文量
19
审稿时长
12 weeks
期刊介绍: The overall aim of the International Journal of Telemedicine and Applications is to bring together science and applications of medical practice and medical care at a distance as well as their supporting technologies such as, computing, communications, and networking technologies with emphasis on telemedicine techniques and telemedicine applications. It is directed at practicing engineers, academic researchers, as well as doctors, nurses, etc. Telemedicine is an information technology that enables doctors to perform medical consultations, diagnoses, and treatments, as well as medical education, away from patients. For example, doctors can remotely examine patients via remote viewing monitors and sound devices, and/or sampling physiological data using telecommunication. Telemedicine technology is applied to areas of emergency healthcare, videoconsulting, telecardiology, telepathology, teledermatology, teleophthalmology, teleoncology, telepsychiatry, teledentistry, etc. International Journal of Telemedicine and Applications will highlight the continued growth and new challenges in telemedicine, applications, and their supporting technologies, for both application development and basic research. Papers should emphasize original results or case studies relating to the theory and/or applications of telemedicine. Tutorial papers, especially those emphasizing multidisciplinary views of telemedicine, are also welcome. International Journal of Telemedicine and Applications employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer-review process.
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