scholarly journals Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

2012 ◽  
Vol 03 (06) ◽  
pp. 1020-1028 ◽  
Author(s):  
Edén A. Alanís-Reyes ◽  
José L. Hernández-Cruz ◽  
Jesús S. Cepeda ◽  
Camila Castro ◽  
Hugo Terashima-Marín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Microcalcification Clusters

Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features

2010 ◽  
pp. 27-53
Author(s):  
Geraldo Braz Júnior ◽  
Leonardo de Oliveira Martins ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Cause Of Deaths ◽  
Cancer Tumor ◽  
Detection And Diagnosis

Breast cancer is a malignant (cancer) tumor that starts from cells of the breast, being the major cause of deaths by cancer in the female population. There has been tremendous interest in the use of image processing and analysis techniques for computer aided detection (CAD)/ diagnostics (CADx) in digital mammograms. The goal has been to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation. CAD/CADx systems can aid radiologists by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of mammograms. This chapter provides an overview of techniques used in computer-aided detection and diagnosis of breast cancer. The authors focus on the application of texture and shape tissues signature used with machine learning techniques, like support vector machines (SVM) and growing neural gas (GNG).

Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features

2012 ◽  
pp. 769-792
Author(s):  
Geraldo Braz Júnior ◽  
Leonardo de Oliveira Martins ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Cause Of Deaths ◽  
Cancer Tumor ◽  
Detection And Diagnosis

Breast cancer is a malignant (cancer) tumor that starts from cells of the breast, being the major cause of deaths by cancer in the female population. There has been tremendous interest in the use of image processing and analysis techniques for computer aided detection (CAD)/ diagnostics (CADx) in digital mammograms. The goal has been to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation. CAD/CADx systems can aid radiologists by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of mammograms. This chapter provides an overview of techniques used in computer-aided detection and diagnosis of breast cancer. The authors focus on the application of texture and shape tissues signature used with machine learning techniques, like support vector machines (SVM) and growing neural gas (GNG).

Machine Learning Techniques for Classification of Breast Cancer

2018 ◽  
pp. 197-200 ◽  
Author(s):  
Ahmed Osmanović ◽  
Sabina Halilović ◽  
Layla Abdel Ilah ◽  
Adnan Fojnica ◽  
Zehra Gromilić
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

2020 ◽  
Vol 60 (1) ◽  
Author(s):  
Matheus Calil Faleiros ◽  
Marcello Henrique Nogueira-Barbosa ◽  
Vitor Faeda Dalto ◽  
José Raniery Ferreira Júnior ◽  
Ariane Priscilla Magalhães Tenório ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Magnetic Resonance ◽  
Machine Learning Techniques ◽  
Resonance Imaging ◽  
Learning Techniques ◽  
Computer Aided

scholarly journals Artificial intelligence applied to support the breast cancer diagnosis

2021 ◽  
Author(s):  
Woldson Leonne Pereira Gomes ◽  
Antonio Silveira
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Cancer Diagnosis ◽  
Final Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cad System ◽  
Non Melanoma Skin Cancer ◽  
Learning Techniques

Breast cancer is a more common neoplasm among women (not considering non-melanoma skin cancer). The estimate for the coming years is still growing and poses a threat to human health. Currently, the methods used in the diagnosis of breast cancer are performed through analysis of mammography images. Allowed, an analysis made by two specialists, which are subject to errors due to factors such as fatigue and lack of capacity. Not only the factor of human errors in diagnoses, certainly the long periods of time until the final diagnosis is another factor to be taken into account, because cancer is a progressive disease over time. In this sense, the present work applied a solution through the automatic classification of mammography images, in order to determine as normal or cancer. In addition, for simulations, two machine learning techniques were added independently, as they can eventually serve as a support in the diagnosis of breast cancer, that is, a CAD system, which means “computer-aided diagnosis”. As machine learning techniques applied for classification referenced as convolutional neural networks and support vector machines. Subsequently, the construction of the classification algorithms, they were subjected to the testing phase, which was found to be more than 85% accurate in the classification of mammography images.

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