Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography

Oliveira, Helder Cesar Rodigues de; Mencattini, Arianna; Casti, Paola; Martinelli, Eugenio; Natale, Corrado Di; Catani, Juliana H.; Barros, Nestor de; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

doi:10.1117/12.2293388

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…These FPs can be removed after the classification stage. In the literature, many researchers have devised automatic [18-21] as well as ensemble segmentation and classification [22-24] algorithms by combining several techniques to reduce the FPs at the detection stage. In general, the segmentation techniques can be characterized as thresholding based, region based (ie, region growing and region clustering), feature and edge based.…”

Section: Methodsmentioning

confidence: 99%

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

Gardezi¹,

Elazab²,

Lei³

et al. 2019

J Med Internet Res

170

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Background Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems. Objective This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks. Methods In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets. Results The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems. Conclusions From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

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Section: Methodsmentioning

confidence: 99%

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

Gardezi¹,

Elazab²,

Lei³

et al. 2019

J Med Internet Res

170

View full text Add to dashboard Cite

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

B¹

2022

JAIMLNN

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Many sectors of research and development use digital image processing to development digital images and extract usable features since the data, whichever may subsequently be castoff to make key judgments by high accuracy. Such approaches were also used in medical solicitations, notably now the identification of breast cancer. 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 System (CAD). 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 after CAD schemes being used toward help radiologists make better diagnoses.

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Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography

Cited by 2 publications

References 11 publications

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

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

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