Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case–control study

• Published in: Medicine (Baltimore) Vol. 100; no. 16; p. e25663
• Main Authors: Choi, Soo Yun, Park, Sunggyun, Kim, Minchul, Park, Jongchan, Choi, Ye Ra, Jin, Kwang Nam
• Format: Journal Article
• Language: English
• Published: United States Lippincott Williams & Wilkins 23.04.2021
• Subjects: Aged; Area Under Curve; Case-Control Studies; Deep Learning - statistics & numerical data; Female; Humans; Lung Diseases - diagnostic imaging; Lung Neoplasms - diagnostic imaging; Male; Middle Aged; Observational Study; Observer Variation; Pneumothorax - diagnostic imaging; Radiographic Image Interpretation, Computer-Assisted - methods; Radiographic Image Interpretation, Computer-Assisted - statistics & numerical data; Radiography, Thoracic - methods; Radiography, Thoracic - statistics & numerical data; Reproducibility of Results; ROC Curve; Thoracic Neoplasms - diagnostic imaging

Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 major thoracic abnormalities visible on chest radiog...