Using Convolutional Neural Network for Chest X-ray Image classification

被引：0
|
作者：
Soric, Matija [1 ,2 ]
Pongrac, Danijela [1 ]
Inza, Inaki [2 ]
机构：
[1] Zagreb Univ Appl Sci, Zagreb, Croatia
[2] Univ Basque Country, Donostia San Sebastian, Spain
来源：
2020 43RD INTERNATIONAL CONVENTION ON INFORMATION, COMMUNICATION AND ELECTRONIC TECHNOLOGY (MIPRO 2020) | 2020年
关键词：
convolutional neural network; classification; deep learning; X-ray imaging;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Chest X-ray is an imaging technique that plays an important role in pneumonia diagnosis. Owing to the high availability of medically-oriented image datasets, great success can be achieved using convolutional neural networks (CNNs) in the recognition and classification of these images. Since previous research has shown CNNs to perform as well as the best clinicians in diagnostic tasks, they caused great excitement among researchers. In this paper, convolutional neural network (CNN) machine learning (ML) model was built using a supervised dataset. The dataset used contained both pneumonia and non-pneumonia images, which the model had to classify correctly. In the end, the model is demonstrated to have achieved satisfactory results, with the high accuracy of 90.38%, 98.21% recall and 87.84% precision.
引用
收藏
页码：1771 / 1776
页数：6
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