Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review

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.

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

Journal of Cancer Therapy ◽

10.4236/jct.2012.36132 ◽

2012 ◽

Vol 03 (06) ◽

pp. 1020-1028 ◽

Cited By ~ 1

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

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

Advances in Bioinformatics and Biomedical Engineering - Biomedical Image Analysis and Machine Learning Technologies ◽

10.4018/978-1-60566-956-4.ch002 ◽

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).

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Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

European Thyroid Journal ◽

10.1159/000504390 ◽

2019 ◽

Vol 9 (4) ◽

pp. 186-193

Author(s):

Lei Xu ◽

Junling Gao ◽

Quan Wang ◽

Jichao Yin ◽

Pengfei Yu ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Deep Learning ◽

Diagnostic Performance ◽

Thyroid Nodules ◽

Computer Aided Diagnosis ◽

Cad Systems ◽

Cad System ◽

Computer Aided ◽

Aided Diagnosis

Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79–0.92], specificity 0.85 [95% CI 0.77–0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91–56.20]; deep learning: sensitivity 0.89 [95% CI 0.81–0.93], specificity 0.84 [95% CI 0.75–0.90], DOR 40.87 [95% CI 18.13–92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78–0.93] vs. 0.87 [95% CI 0.85–0.89], specificity 0.85 [95% CI 0.76–0.91] vs. 0.87 [95% CI 0.81–0.91], DOR 40.12 [95% CI 15.58–103.33] vs. DOR 44.88 [95% CI 30.71–65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

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