A study on mammography computer aided diagnosis system using machine learning methods

2013 ◽  
Author(s):  
K. Dinakaran ◽  
V. Sivakrithika
Keyword(s):  
Machine Learning ◽  
Computer Aided Diagnosis ◽  
Learning Methods ◽  
Diagnosis System ◽  
Machine Learning Methods ◽  
Computer Aided ◽  
Aided Diagnosis

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.

scholarly journals Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1390
Author(s):  
Mohamed A. Kassem ◽  
Khalid M. Hosny ◽  
Robertas Damaševičius ◽  
Mohamed Meselhy Eltoukhy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Skin Lesion ◽  
Ad Hoc ◽  
Racial Bias ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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