An Overview of Deep Learning Approaches in Chest Radiograph

Abstract

Chest X-ray (CXR) interpretations are conducted in hospitals and medical facilities on daily basis. If the interpretation tasks were performed correctly, various vital medical conditions of patients can be revealed such as pneumonia, pneumothorax, interstitial lung disease, heart failure and bone fracture. The current practices often involve tedious manual processes dependent on the expertise of radiologist or consultant, thus, the execution is easily prone to human errors of being misdiagnosed. With the recent advances of deep learning and increased hardware computational power, researchers are working on various networks and algorithms to develop machines learning that can assists radiologists in their diagnosis and reduce the probability of misdiagnosis. This paper presents a review of deep learning advancements made in the field of chest radiography. It discusses single and multi-level localization and segmentation techniques adopted by researchers for higher accuracy and precision.

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

“…With an enormous number of COVID-19 cases suspected daily, it is difficult to assign enough time and resources to individual radiographs. This discrepancy between the available experts and the need of the human expertise has promoted automation and machine learning to fill this much-needed gap [8]. Over the last year, scientists and researchers are unitedly working to automate the detection methods and provide intelligent machines that can easily distinguish infectious COVID-19 cases from other similar appearing cases.…”

Reliability Metrics of Explainable CNN based on Wasserstein Distance for Cardiac Evaluation

“…Previous research has also estimated the cardiac state using deep learning, as in the following cases: left cardiac chamber enlargement detection [15], cardiomegaly detection [16], and heart failure detection [17,18]. Moreover, there are extensive reviews on the techniques and results of inputting chest X-ray images into a CNN [19,20]. Some of these have the function outputting activated regions as reason of estimation [12][13][14][15]17].…”

A Comparative Study of Multiple Neural Network for Detection of Covid 19 on Chest X-Ray

“…In terms of early detection, although X-ray diagnosis has some benefits over the current RT-PCR method, however, the biggest challenge encounter here is that this method requires medical expert to comprehend the X-ray images and the extraction of important findings take a lot of valuable time [7]. In the current pandemic situation when everyday there is huge amount of people to be screened, it would be a huge burden to radiologists.…”

“…With the recent advancement in computing power, hardware and availability of medical image datasets; artificial intelligence and machine learning have taken a boom over the past few years. Over the last year, scientists and researchers are globally working on the automated methods that used contemporary AI technologies, particularly deep learning, to enhance the performance of Xray imaging with the goal to reduce the workload of radiologists [7]. Studies have been conducted to use neural networks to differentiate Covid-19 X-ray from that of pneumonia and normal X-ray.…”