Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks

By Mohammad Tariqul Islam, Md Abdul Aowal, Ahmed Tahseen Minhaz, Khalid Ashraf. 2017

207 citations recorded by the source corpus.

Chest X-Rays (CXRs) are widely used for diagnosing abnormalities in the heart and lung area. Automatically detecting these abnormalities with high accuracy could greatly enhance real world diagnosis processes. Lack of standard publicly available dataset and benchmark studies, however, makes it difficult to compare various detection methods. In order to overcome these difficulties, we have used a publicly available Indiana CXR, JSRT and Shenzhen dataset and studied the performance of known deep convolutional network (DCN) architectures on different abnormalities. We find that the same DCN architecture doesn't perform well across all abnormalities. Shallow features or earlier layers consistently provide higher detection accuracy compared to deep features. We have also found ensemble models to improve classification significantly compared to single model. Combining these insight, we report the highest accuracy on chest X-Ray abnormality detection on these datasets. We find that for cardiomegaly detection, the deep learning method improves the accuracy by a staggering 17 percentage point compared to rule based methods. We applied the techniques to the problem of tuberculosis detection on a different dataset and achieved the highest accuracy. Our localization experiments using these trained classifiers show that for spatially spread out abnormalities like cardiomegaly and pulmonary edema, the network can localize the abnormalities successfully most of the time. One remarkable result of the cardiomegaly localization is that the heart and its surrounding region is most responsible for cardiomegaly detection, in contrast to the rule based models where the ratio of heart and lung area is used as the measure. We believe that through deep learning based classification and localization, we will discover many more interesting features in medical image diagnosis that are not considered traditionally.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning (2017, neutral): “Islam et al. (2017)”
An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare (2019, unknown): “e performance of several convolutional models on diverse abnormalities relying on the publicly available OpenI dataset [17] found that the same deep convolutional network architecture does not perform well across all abnormalities [18], ensemble models significantly improved classification accuracy ”
Pneumonia Detection Using CNN based Feature Extraction (2019, methodology): “[14] using the publicly available OpenI dataset [15].”
Computer-aided detection in chest radiography based on artificial intelligence: a survey (2018, methodology): “[111] proposed the attention guided convolutional neural network (AG-CNN), which has three branches, i.e., global branch, local branch, and fusion branch.”
COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, background): “…al., 2018; Islam and Zhang, 2018], tuberculosis and cancer analysis [Gordienko et al., 2018; Stirenko et al., 2018; Lakhani and Sundaram, 2017], abnormality detection [Islam et al., 2017], explanation [Singla et al., 2020], and multi-modality predictions Rubin et al. [2018]; Hashir et al. [2020].”
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018, background): “Islam et al. [34] explore different CNN architectures and ﬁnd that a single CNN does not perform well across all abnormalities.”
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization (2019, methodology): “Previous publications using deep learning adopt deep learning models such as AlexNet, GoogLeNet and ResNet, which were developed for natural images classifications tasks 5–7 , 18 .”