Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …

Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly
to medical imaging, their applications increased significantly to become a trend. Likewise …

Deep learning (DL) has shown rapid advancement and considerable promise when applied
to the automatic detection of diseases using CXRs. This is important given the widespread …

Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic
radiology, are under active investigation with deep learning technology, which has shown …

A new generalized framework for lung cancer detection and classification are introduced in
this paper. Specifically, two types of deep models are presented. The first model is a …

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death
rate is increasing step by step. There are chances of recovering from lung cancer by …

Background A computer-aided detection (CAD) system may help surveillance for pulmonary
metastasis at chest radiography in situations where there is limited access to CT. Purpose …