Efficient Scaling of Convolutional Neural Network for Detecting and Classifying Pneumonia Disease

Sofia Sa’idah, I. P. Y. N. Suparta, Syifa Rezki Fauziah
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Abstract

Lung is one of vital human organ. When lung is suffered by any cause, it will impact on the body's metabolic processes. One of disorder in the lung is pneumonia. Pneumonia is caused by pathogenic microorganisms, namely bacteria, viruses, and fungi. In this study, pneumonia diseases are classified using deep learning method, which is EfficientNet Architecture Convolutional Neural Network. This study is using secondary data which 2430 data were used. About 486 data were used for testing process and 1944 data used for training process. By using this method, it can be concluded that the system designed is able to classify 3 types of x-ray images. The results obtained in this study are 89.09% accuracy and 1.8934 loss. For other parameters such as f-1 score, recall and precision, the average value for each are 0.87;0.91 and 0.89.
卷积神经网络在肺炎疾病检测与分类中的高效缩放
肺是人体重要器官之一。当肺部受到任何原因的影响时,都会对人体的代谢过程产生影响。肺部疾病之一是肺炎。肺炎是由病原微生物引起的,即细菌、病毒和真菌。在本研究中,肺炎疾病分类使用深度学习方法,即高效网络架构卷积神经网络。本研究采用二手资料,共使用2430份资料。大约486个数据用于测试过程,1944个数据用于训练过程。通过这种方法,可以得出设计的系统能够对3种类型的x射线图像进行分类。本研究获得的结果准确率为89.09%,损失率为1.8934。其他参数如f-1分、查全率和查准率的平均值分别为0.87、0.91和0.89。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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