As one of the most ubiquitous diagnostic imaging tests in medical practice, chest
radiography requires timely reporting of potential findings and diagnosis of diseases in the …

As one of the most ubiquitous diagnostic imaging tests in medical practice, chest
radiography requires timely reporting of potential findings and diagnosis of diseases in the …

Our objective is to evaluate the effectiveness of efficient convolutional neural networks
(CNNs) for abnormality detection in chest radiographs and investigate the generalizability of …

Aim To develop a machine learning-based model for the binary classification of chest
radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting …

Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to
errors. An automated system capable of categorizing chest radiographs based on the …

Objectives Convolutional neural networks (CNNs) are a subtype of artificial neural network
that have shown strong performance in computer vision tasks including image classification …

Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-
performance automated binary classification of chest radiographs. Materials and Methods In …

Chest radiography is a widely used diagnostic imaging procedure in medical practice, which
involves prompt reporting of future imaging tests and diagnosis of diseases in the images. In …

AIM To investigate the performance of a deep-learning approach termed lesion-aware
convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X …

Background Deep learning (DL) based solutions have been proposed for interpretation of
several imaging modalities including radiography, CT, and MR. For chest radiographs, DL …