Current status and future directions of computer-aided diagnosis in mammography

2007 ◽  
Vol 31 (4-5) ◽  
pp. 224-235 ◽  
Author(s):  
Robert M. Nishikawa
Keyword(s):  
Computer Aided Diagnosis ◽  
Current Status ◽  
Future Directions ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

2019 ◽  
Vol 4 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Omer F Ahmad ◽  
Antonio S Soares ◽  
Evangelos Mazomenos ◽  
Patrick Brandao ◽  
Roser Vega ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Diagnosis ◽  
Current Evidence ◽  
Future Directions ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

2019 ◽  
Vol 8 (4) ◽  
pp. 12261-12273
Keyword(s):  
Capsule Endoscopy ◽  
Computer Aided Diagnosis ◽  
Current Status ◽  
Human Beings ◽  
Abnormality Detection ◽  
Gi Tract ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

scholarly journals A Review on Computer Aided Diagnosis of Acute Brain Stroke

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8507
Author(s):  
Mahesh Anil Inamdar ◽  
Udupi Raghavendra ◽  
Anjan Gudigar ◽  
Yashas Chakole ◽  
Ajay Hegde ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Computer Aided Diagnosis ◽  
Current Status ◽  
Research Areas ◽  
Current Requirement ◽  
Common Causes ◽  
Computer Aided ◽  
Brain Stroke ◽  
Meta Analyses ◽  
Aided Diagnosis

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

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