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A SYSTEMATIC REVIEW OF CAD SYSTEM BASED APPROACH IN DIAGNOSING BREAST CANCER AND ANALYZE EFFECTIVENESS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS IN EARLY DETECTION

2021 International Journal of Biology Pharmacy and Allied Sciences  
This paper will focus on the prediction of the test samples to be malignant or not by studying the ways of performing machine learning based computer aided systems.  ...  This system consists of the different stages as in image pre-processing, segmentation of images, extraction of relevant features and image classification. We also found from the review that the  ...  common for every machine learning method that is used to predict cancer and supervised learning algorithms are on the basis of some criteria and conditions.  ... 
doi:10.31032/ijbpas/2021/10.11.1069 fatcat:l4qh2lcnbbeobj7ccriijgqynq

Prediction of Breast Cancer using Machine Learning Approaches

Reza Rabiei
2022 Journal of Biomedical Physics and Engineering  
Machine learning has the potential to predict breast cancer based on features hidden in data.  ...  The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer.  ...  Conclusion The proposed machine-learning approaches could predict breast cancer as the early detection of this disease could help slow down the progress of the disease and reduce the mortality rate through  ... 
doi:10.31661/jbpe.v0i0.2109-1403 pmid:35698545 pmcid:PMC9175124 fatcat:frmlfahx7zf7nhld6a2sgnftl4

Current Applications and Future Impact of Machine Learning in Radiology

Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E. Samir, Oleg S. Pianykh, J. Raymond Geis, Pari V. Pandharipande, James A. Brink, Keith J. Dreyer
2018 Radiology  
Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings  ...  In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology.  ...  The saliency maps could provide "explicability" for the machine learning models and improve the accuracy for detection of findings (83) .  ... 
doi:10.1148/radiol.2018171820 pmid:29944078 pmcid:PMC6542626 fatcat:vrf37sksivfkph47qdicwzeqge

Breast Cancer Detection and Diagnosis Using Machine Learning: A Survey

Riyadh M. Al-Tam, Sachin M. Narangale
2021 Journal of scientific research  
Recently, many hardware and software have been applying different techniques for achieving high-quality results, especially the techniques of machine learning.  ...  In this paper, a comprehensive survey to review most of the accurate techniques being used for both detecting and diagnosing breast cancer is conducted.  ...  Finally, Vara is implemented using machine learning techniques, can be used as a viewer and reporting product for mammography images (Vara, 2020) .  ... 
doi:10.37398/jsr.2021.650532 fatcat:r4mo24373rd2pe7ooc3gnwiwue

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives

Krzysztof J. Geras, Ritse M. Mann, Linda Moy
2019 Radiology  
Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography.  ...  In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges.  ...  Conclusions The development and implementation of artificial intelligence (AI) for mammography has been ongoing for several decades.  ... 
doi:10.1148/radiol.2019182627 pmid:31549948 pmcid:PMC6822772 fatcat:mpfjqcwd7bfvdgjl4oe7utoiiq

Classification methods, Deep Learning Architecture, Data source and Challenges in Detection of Breast Cancer

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Different types of cancer can be prevented, screened for and/or detected and treated at an early stage. According to recent statistics breast cancer has a mortality rate of 12.7 per one lakh women.  ...  Mutation of genes at an abnormal rate leads to cancer. Changes in the size, color, texture and constant pain are the initial symptoms of breast cancer.  ...  Highest accuracy of SVM classifier was 97.9 % reported in [12] . A supervised learning algorithm creates an ensemble of decision tress and the system is trained using the bagging method [14] .  ... 
doi:10.35940/ijitee.b1123.1292s19 fatcat:ccfv2k34dfevtd74yggm4vmywm

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Epimack Michael, He Ma, Hong Li, Frank Kulwa, Jing Li, Paul Harrison
2021 BioMed Research International  
The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing.  ...  Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many  ...  Acknowledgments The authors would like to express their deep gratitude to Oguti Ann Move for her proofreading work and would also like to thank the insightful comments and suggestions from Prof.  ... 
doi:10.1155/2021/9962109 pmid:34337066 pmcid:PMC8321730 fatcat:j6rfda6zevgbhe7h7smw5lbexm

Artificial Intelligence in Medical Imaging of the Breast

Yu-Meng Lei, Miao Yin, Mei-Hui Yu, Jing Yu, Shu-E Zeng, Wen-Zhi Lv, Jun Li, Hua-Rong Ye, Xin-Wu Cui, Christoph F. Dietrich
2021 Frontiers in Oncology  
Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients.  ...  This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast  ...  ACKNOWLEDGMENTS I would like to extend my sincere gratitude to my colleagues for their help in the completion of this article and the reviewers for reviewing my article.  ... 
doi:10.3389/fonc.2021.600557 fatcat:5tphtisnhnd33c3e5oycvywnee

Anomaly Detection in Medical Imaging – A Mini Review [article]

Maximilian E. Tschuchnig, Michael Gadermayr
2021 arXiv   pre-print
The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts.  ...  Also, the successful and substantial amount of research in the brain MRI domain shows the potential for applications in further domains like OCT and chest X-ray.  ...  between the learned healthy and the lesioned latent space [10] .  ... 
arXiv:2108.11986v1 fatcat:lzffrlo7ovfppiejztkafzlpyq

Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images

Redona Brahimetaj, Inneke Willekens, Annelien Massart, Ramses Forsyth, Jan Cornelis, Johan De Mey, Bart Jansen
2022 BMC Cancer  
Machine learning algorithms were used to diagnose: (a) individual microcalcifications, (b) samples.  ...  Background The detection of suspicious microcalcifications on mammography represents one of the earliest signs of a malignant breast tumor.  ...  the consistency of results over different machine learning algorithms.  ... 
doi:10.1186/s12885-021-09133-4 pmid:35148703 pmcid:PMC8832731 fatcat:uahxconlajepfh2hco6avgt77m

BREAST CANCER DETECTION USING MAMMOGRAM FEATURES USING RANDOM FOREST ALGORITHM

2020 International journal for advanced research in science & technology  
The machine learning algorithm takes many of these samples, called the training set, and builds an internal model. This built model is used to classify and predict the data.  ...  Machine learning is the subfield of computer science that studies programs that generalize from past experience.  ...  Whereas in random forest algorithm, Instead of using information gain or gini index for calculating the root node, the process of finding the root node and splitting the feature nodes will happen randomly  ... 
doi:10.48047/ijarst/v10/i11/02 fatcat:vkhksfa6izb3jesh2curcjvrgu

Computational assessment of breast tumour differentiation using multimodal data

Jean Rossario Raj, Syed Mohammed Khalilur Rahman, Sneh Anand
2016 Informatics in Medicine Unlocked  
The developed model was tested using supervised learning algorithms with three classifiers for 210 cases by comparing the results with the gold standard biopsy results.  ...  Early detection of breast cancer requires accurate prediction and reliable diagnostic modalities.  ...  The authors declare no conflict of interest in this study, the funding source has no role in the preparation of the manuscript, and the content is solely the responsibility of the authors.  ... 
doi:10.1016/j.imu.2016.04.001 fatcat:axsnp6i4yffolpse2tstb4p5pi

NLP Algorithms Endowed for Automatic Extraction of Information from Unstructured Free-Text Reports of Radiology Monarchy

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
, supervised machine learning algorithm and many more.  ...  Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations  ...  In the paper "An enhanced CRFs-based system for information extraction from radiology reports", [3] ,author studied set of 500 free-text mammography reports [30] using supervised machine learning algorithm  ... 
doi:10.35940/ijitee.l8009.1091220 fatcat:sjth33dnvjfnhn442figt75llq

Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection

Nursabillilah Mohd Ali, Nor Azlina Ab Aziz, Rosli Besar
2020 Indonesian Journal of Electrical Engineering and Computer Science  
Preliminary research on the usage of machine learning classifier and feature selection method for breast cancer classification is conducted here.  ...  Despite the advancement of medical diagnostic and prognostic tools for early detection and treatment of breast cancer patients, research on development of better and more reliable tools is still actively  ...  Also, thanks to the referred reviewers for their valuable comments and suggestions that made considerable improvement to our work.  ... 
doi:10.11591/ijeecs.v20.i2.pp712-719 fatcat:za3olh3donaz3htjqbym243xcy

Survey on the Detection of Breast Tumour by Thermography

R. Krishna Bharathi
2018 International Journal for Research in Applied Science and Engineering Technology  
The sensitivity and specificity of mammograms remain less than optimal, especially for patients with dense breast tissue.  ...  This paper gives the survey on some of the image processing methods or the processes that are involved in the breast cancer detection from thermal images.  ...  the survey on breast cancer diagnosis using various machine learning algorithms and methods, which are used to improve the accuracy of predicting cancer.  ... 
doi:10.22214/ijraset.2018.3432 fatcat:js5vndovtjdpzg64yxg6ebypgq
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