Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples

2007 ◽  
Vol 37 (6) ◽  
pp. 1088-1098 ◽  
Author(s):  
Ming Li ◽  
Zhi-Hua Zhou
Keyword(s):  
Machine Learning ◽  
Computer Aided Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 25407-25419 ◽  
Author(s):  
Alvaro Sobrinho ◽  
Andressa C. M. Da S. Queiroz ◽  
Leandro Dias Da Silva ◽  
Evandro De Barros Costa ◽  
Maria Eliete Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Developing Countries ◽  
Comparative Analysis ◽  
Kidney Disease ◽  
Computer Aided Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Survey on machine learning algorithms for liver disease diagnosis and prediction

2018 ◽  
Vol 7 (1.8) ◽  
pp. 99 ◽  
Author(s):  
M Kiran Kumar ◽  
M Sreedevi ◽  
Y C. A. Padmanabha Reddy
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

Machine learning plays a vital role in health care industry. It is very important in Computer Aided Diagnosis. Computer Aided Diagnosis is a quickly developing dynamic region of research in medicinal industry. The current specialists in machine learning guarantee the enhanced precision of discernment and analysis of diseases. The computers are empowered to think by creating knowledge by learning. This procedure enables the computers to self-learn individually without being explicitly programed by the programmer .There are numerous sorts of Machine Learning Techniques and which are utilized to classify the data sets. They are Supervised, Unsupervised and Semi-Supervised, Reinforcement, deep learning algorithms. The principle point of this paper is to give comparative analysis of supervised learning algorithms in medicinal area and few of the techniques utilized as a part of liver disease prediction.

scholarly journals The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 694
Author(s):  
Xuejiao Pang ◽  
Zijian Zhao ◽  
Ying Weng
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Practice ◽  
Gastrointestinal Endoscopy ◽  
Computer Aided Diagnosis ◽  
Learning Methods ◽  
Learning Abilities ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

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