A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases

被引：65

作者：

Allaouzi, Imane ^{[1
]}

Ben Ahmed, Mohamed ^{[1
]}

机构：

[1] Abdelmalek Essaadi Univ, LIST FSTT, Tangier 90040, Morocco

来源：

IEEE ACCESS | 2019年 / 7卷

关键词：

CAD; CXR; transfer learning; CNN; computer vision; multi-label classification; problem transformation method; deep learning; image classification; image feature extraction; thoracic pathologies;

D O I：

10.1109/ACCESS.2019.2916849

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Chest X-ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of the CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of the problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (frontal view) and CheXpert (frontal and lateral views). The results show that our proposed method outperformed the current state of the art.

引用

页码：64279 / 64288

页数：10

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