Computer aided diagnosis based on medical image processing and artificial intelligence methods

2006 ◽  
Vol 569 (2) ◽  
pp. 591-595 ◽  
Author(s):  
John Stoitsis ◽  
Ioannis Valavanis ◽  
Stavroula G. Mougiakakou ◽  
Spyretta Golemati ◽  
Alexandra Nikita ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Computer Aided Diagnosis ◽  
Artificial Intelligence Methods ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Implementation of Image Processing Technique for Diagnosis of Diseases

2018 ◽  
pp. 237-246
Author(s):  
Kalyani S. Boral ◽  
V. T. Gaikwad
Keyword(s):  
Image Processing ◽  
Image Classification ◽  
Medical Image ◽  
Image Data ◽  
Computer Aided Diagnosis ◽  
Image Processing Technique ◽  
Computer Aided ◽  
Medical Image Data ◽  
Aided Diagnosis ◽  
Diagnosis Of Diseases

Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages of diseases. Medical informatics is the study that combines two medical data sources: biomedical record and imaging data. Medical image data is formed by pixels that correspond to a part of a physical object and produced by imaging modalities. discovery of medical image data methods is a challenge in the sense of getting their insight value, analyzing and diagnosing of a specific disease. Image classification plays an important role in computer-aided-diagnosis of diseases and is a big challenge on image analysis tasks. This challenge related to the usage of methods and techniques in exploiting image processing result, pattern recognition result and classification methods and subsequently validating the image classification result into medical expert knowledge. The main objective of medical images classification is to reach high accuracy to identify the name of disease. It showed the improvement of image classification techniques such as to increase accuracy and sensitivity value and to be feasible employed for computer-aided-diagnosis are a big challenge and an open research.

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 MAGAN: Mask Attention Generative Adversarial Network for Liver Tumor CT Image Synthesis

2020 ◽  
Author(s):  
Yang Liu ◽  
Lu Meng ◽  
Jianping Zhong
Keyword(s):  
Liver Tumor ◽  
Medical Image ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Generative Adversarial Network ◽  
Liver Ct ◽  
Adversarial Network ◽  
Computer Aided ◽  
Image Dataset ◽  
Aided Diagnosis

Abstract Background: For deep learning, the size of the dataset greatly affects the final training effect. However, in the field of computer-aided diagnosis, medical image datasets are often limited and even scarce.Methods: We aim to synthesize medical images and enlarge the size of the medical image dataset. In the present study, we synthesized the liver CT images with a tumor based on the mask attention generative adversarial network (MAGAN). We masked the pixels of the liver tumor in the image as the attention map. And both the original image and attention map were loaded into the generator network to obtain the synthesized images. Then the original images, the attention map, and the synthesized images were all loaded into the discriminator network to determine if the synthesized images were real or fake. Finally, we can use the generator network to synthesize liver CT images with a tumor.Results: The experiments showed that our method outperformed the other state-of-the-art methods, and can achieve a mean peak signal-to-noise ratio (PSNR) as 64.72dB.Conclusions: All these results indicated that our method can synthesize liver CT images with tumor, and build large medical image dataset, which may facilitate the progress of medical image analysis and computer-aided diagnosis.

Sign in / Sign up

Export Citation Format

Share Document