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A Review of Computer-Aided Detection and Diagnosis of Breast Cancer in Digital Mammography

Luqman Mahmood Mina, Nor Ashidi Mat Isa
2015 Journal of Medical Sciences  
Mammographic screening has been shown to be effective in reducing breast cancer mortality rates by 30-70%, as confirmed from available screening programs.  ...  However, mammograms are difficult to interpret, especially in the screening of physical aberrations.  ...  The recall rate increased from 6.5-7.7% and the positive-predictive value of biopsy remained constant at 38%.  ... 
doi:10.3923/jms.2015.110.121 fatcat:s2qvzcp7hndtdkrbwyv3rdz3bq

Using multiscale texture and density features for near-term breast cancer risk analysis

Wenqing Sun, Tzu-Liang Bill Tseng, Wei Qian, Jianying Zhang, Edward C. Saltzstein, Bin Zheng, Fleming Lure, Hui Yu, Shi Zhou
2015 Medical Physics (Lancaster)  
texture and density feature analysis of digital mammograms to predict near-term breast cancer risk.  ...  Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected.  ...  ACKNOWLEDGMENTS This work was partially supported by National Institutes of Health (SC1CA166016), National Science Foundation (DUE-TUES-1246050), and Department of Education (Award No. P031S120131).  ... 
doi:10.1118/1.4919772 pmid:26127038 pmcid:PMC4441716 fatcat:3aqg5cgjmbggtbtnqklr746csa

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

Krzysztof J. Geras, Ritse M. Mann, Linda Moy
2019 Radiology  
of the predictions of the models.  ...  Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic  ...  Essentials n In clinical practice, the use of computer-aided diagnosis (CAD) does not improve diagnostic accuracy because the many false prompts lead to higher false-positive rates, recall rates, and biopsy  ... 
doi:10.1148/radiol.2019182627 pmid:31549948 pmcid:PMC6822772 fatcat:mpfjqcwd7bfvdgjl4oe7utoiiq

Machine Learning Techniques for Diagnosis of Breast Cancer

Alfonso Rojas Domínguez
2020 Research on computing science  
Early detection of breast cancer can be achieved through mammography screening programs. Studies have shown that double reading of mammograms improves the detection of breast abnormalities.  ...  Some of the signs associated with breast cancer that can be observed in mammograms include: masses, calcifications, distortion of the parenchimal tissue, asymmetry of breast tissue between the breasts  ...  This work was supported by the National Council of Science and Technology of Mexico (CONACYT) under Research Grant C ÁTEDRAS-2598 (A. Rojas).  ... 
dblp:journals/rcs/Dominguez20 fatcat:d6aei22j5jbhrjsmmluz6x5iea

Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms

Wenqing Sun, Bin Zheng, Fleming Lure, Teresa Wu, Jianying Zhang, Benjamin Y. Wang, Edward C. Saltzstein, Wei Qian
2014 Computerized Medical Imaging and Graphics  
The purpose of this study is to design and test the Global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in  ...  the next sequential screening mammography examination.  ...  and the associated high false-positive recall rates contribute to Page | 4 higher cost as well as the unnecessary harms to many cancer-free women who routinely participate in the recommended mammography  ... 
doi:10.1016/j.compmedimag.2014.03.001 pmid:24725671 fatcat:4vojj25zfffs3etxild6v5klzy

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Meredith A. Jones, Warid Islam, Rozwat Faiz, Xuxin Chen, Bin Zheng
2022 Frontiers in Oncology  
image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment.  ...  the development of new AI-based models of breast images that cover a broad range of application topics.  ...  radiologists decrease the false-positive recall rates in future clinical practice (Table 3 ).  ... 
doi:10.3389/fonc.2022.980793 pmid:36119479 pmcid:PMC9471147 fatcat:xc5fb7xh4bbwrgjtd4j5ynvjpq

Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

João Mendes, Nuno Matela
2021 Journal of Imaging  
This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk.  ...  In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jimaging7060098 fatcat:q3vxbpkdfzeflngwbptp4n3xn4

Advances in computer-aided diagnosis for breast cancer

Lubomir Hadjiiski, Berkman Sahiner, Heang-Ping Chan
2006 Current Opinion in Obstetrics and Gynecology  
The aim of this review is to discuss the current state of the CAD systems for breast cancer diagnosis, their application as a second reader in clinical practice, and the studies that evaluated the effect  ...  The main clinical use of CAD to date is for screen-film mammography due to the commercially available FDA approved systems. Many studies showed that CAD improves radiologists' performance.  ...  Morphological and texture features were extracted. A feature classifier was designed to differentiate true masses from normal tissues.  ... 
doi:10.1097/01.gco.0000192965.29449.da pmid:16493263 pmcid:PMC2800983 fatcat:upg6yoyunzei7dyz4q5mn7ds6e

An Exploration On Mammographic Image Abnormality Using Computer Aided Detection (Cad) System

Dr. Venkateswarulu Naik. B
2022 Journal of Artificial Intelligence, Machine Learning and Neural Network  
This paper provides a brief overview of the Computer Aided Detection System, whichever could remain used as a choice sustenance system for the automatic detection for breast cancer, with the predictions  ...  Due to the limits of human observation, computers obligate frolicked a vital part in identifying initial signs to cancer over the years, resulting in the creation of a high-accuracy Computer Aided Detection  ...  Features were extracted in this phase to categorize nature of breast tissue to discriminate normal and cancerous breasts with gray level co-occurrence matrix.  ... 
doi:10.55529/jaimlnn.22.1.7 fatcat:on3m6i2vg5dvlkuzmlrzlpdely

Breast Cancer Detection and Diagnosis Using Machine Learning: A Survey

Riyadh M. Al-Tam, Sachin M. Narangale
2021 Journal of scientific research  
In this paper, a comprehensive survey to review most of the accurate techniques being used for both detecting and diagnosing breast cancer is conducted.  ...  Besides, different commercial and non-commercial hardware and software are mentioned with their advantages and disadvantages in the process of detecting and diagnosing breast lesions.  ...  of false-positive was reduced.  ... 
doi:10.37398/jsr.2021.650532 fatcat:r4mo24373rd2pe7ooc3gnwiwue

A Novel Approach to Mammogram Classification using Spatio-Temporal and Texture Feature Extraction using Dictionary based Sparse Representation Classifier

Vaishali D. Shinde, B. Thirumala
2020 International Journal of Advanced Computer Science and Applications  
In order to predict the breast cancer, mammogram is considered as a promising technique which helps to identify the early stages of cancer.  ...  In the next phase of work, we developed a mixed strategy of feature extraction where we consider Gray Level Cooccurrence Matrix (GLCM), Histogram of oriented Gradients (HoG) with Principal Component Analysis  ...  of breast cancer and reduction in false positive rates using machine vision-based solution.  ... 
doi:10.14569/ijacsa.2020.0111041 fatcat:ostbwzbl6femjnuhv7xwunyxnu

AI in Breast Cancer Imaging: A Survey of Different Applications

João Mendes, José Domingues, Helena Aidos, Nuno Garcia, Nuno Matela
2022 Journal of Imaging  
This paper surveys different applications of AI in Breast Imaging.  ...  Following that, researches in the field of breast cancer risk prediction using mammograms—which may be able to allow screening programs customization both on periodicity and modality—are reviewed.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jimaging8090228 pmid:36135394 pmcid:PMC9502309 fatcat:vdochhposbaw7gwhiq6vlh5kga

A State-of-the-art Survey on Deep Learning Methods for Detection of Architectural Distortion from Digital Mammography

Olaide N. Oyelade, Absalom E. Ezugwu
2020 IEEE Access  
This is evidenced by the application of curvature Gabor filter in human authentication [39] ; extraction of features of masses from mammography using Gabor filter and Cuckoo search algorithm [40] ; extraction  ...  in breast tissues.  ...  Author Name: Preparation of Papers for IEEE Access (February 2017) VOLUME XX, 2017 CONFLICT OF INTEREST The authors declare that there is no conflict of interests regarding the publication of the paper  ... 
doi:10.1109/access.2020.3016223 fatcat:h2khlie66zggffe5dezuj3o5aa

A new approach to develop computer-aided detection schemes of digital mammograms

Maxine Tan, Wei Qian, Jiantao Pu, Hong Liu, Bin Zheng
2015 Physics in Medicine and Biology  
After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral  ...  The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography  ...  Acknowledgments This work is supported in part by Grant R01 CA160205 from the National Cancer Institute, National Institutes of Health.  ... 
doi:10.1088/0031-9155/60/11/4413 pmid:25984710 pmcid:PMC4459586 fatcat:yxbvy477lnfurazzil5ba4b4cq

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Afsaneh Jalalian, Syamsiah Mashohor, Rozi Mahmud, Babak Karasfi, M Iqbal B Saripan, Abdul Rahman B Ramli
2017 EXCLI Journal : Experimental and Clinical Sciences  
The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification.  ...  Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed.  ...  In medical application, the low rate of false positive and false negative detection is very important.  ... 
doi:10.17179/excli2016-701 pmid:28435432 pmcid:PMC5379115 fatcat:xzcj7igo3fbotfo5b4n3x7izay
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