Retrieval Driven Classification for Mammographic Masses

Kiruthika, K.; Vijayan, D.; Lavanya, R.

doi:10.1109/iccsp.2019.8698044

Cited by 9 publications

(1 citation statement)

References 15 publications

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“…Mammography is a process employed for serving the purpose of detecting breast cancer at early stages by detecting the presence of mass in the breast tissue [3,4]. In breast tissues that are dense, mammograms might deliver incorrect results as it is harder to detect the presence of mass in them [5].…”

Section: Introductionmentioning

confidence: 99%

Computer aided diagnosis system for breast density classification in mammograms

Shrinithi

Vijayan

Lavanya

2022

J. Phys.: Conf. Ser.

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Breast cancer is a deadly disease affecting women around the globe. Mass detection in the breast tissue at an early stage can lessen the mortality rate occurring due to breast cancer. Through mammograms, the presence of masses can be detected at an early stage, however, it’s sensitivity and specificity are limited in the case of dense tissues. Identification of the breast density type prior to the detection of mass can lessen the chance of misclassifying a breast tissue as normal or abnormal, which eventually decreases the false negative and false positive rate. The proposed system classifies breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS). The proposed method has explored the effect of local descriptors on breast density classification and various feature-classifier combinations have also been explored for the classification. The proposed method validated on 624 mammograms from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database has produced an accuracy of 73% for multi-class breast density classification using the speeded-up robust features (SURF) and support vector machine (SVM) classifier.

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Section: Introductionmentioning

confidence: 99%