Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks

Abstract

According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for classification of breast tissue and mass detection.

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

“…As shown in Table 1, studies were generally based on relatively modest numbers of images (and hence smaller number of subjects), except for each of the studies from Kooi et al and Ayer et al [18,31] which investigated AI systems using relatively large datasets (>40,000 images, or mammographic examinations from >9,000 women). Most studies provided limited information on the methods used to assemble the source imaging datasets and the extent that these were verified in terms of a reference standard, with many studies simply citing the source image dataset [10,12,13,19,20,[22][23][24]26,27,30]. However, several studies described an appropriate reference standard that included histopathology with either clinical follow-up or cancer registry matching to ascertain outcomes [9,14,16,18,21,28,31].…”

“…However, several studies described an appropriate reference standard that included histopathology with either clinical follow-up or cancer registry matching to ascertain outcomes [9,14,16,18,21,28,31]. Studies proposed to develop and/or evaluate AI models or techniques for breast cancer detection [9,11,18,21,22,27,28,26], or for diagnosis (classification) or interpretation of mammographic examinations [13,14,15,16,20,[23][24][25]30], or dealt with advancing computer-aided detection (CAD) systems through new AI models [10,12,17,19,29]; and one study investigated AI for discrimination between benign and cancerous lesions jointly with cancer risk prediction [31]. Rodriguez-Ruiz et al [9] reported a multi-reader study comparing an AI system with radiologists' interpretation of various datasets of screening and clinical mammographic examinations.…”

Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review

“…Although many studies focused on benign and malignant differentiation, tumour localisation was another common topic. Five studies showed detecting a mass in dense breast regions and pectoral muscles to be difficult due to high intensities …”

“…Diniz in 2018 classified breast tissue by density and regions with or without a mass; by combining them, they localised and detected masses in dense (Cooper’s ligaments, mammillary glands and ducts) and non‐dense tissue (adipose tissue). Sensitivity was slightly higher in non‐dense tissue (0.9156 vs. 0.9036), yet dense tissue had significantly higher specificity (0.9635 vs. 0.9073), meaning that their CNN was better at recognising negative non‐dense images but identified abnormalities more accurately in dense tissues …”

“…Studies which used imaging modalities other than mammography were excluded. A total of 33 relevant peer‐reviewed articles were found . To compare the results among studies, relevant features were extracted: (1) author and publication year; (2) study aim; (3) utilised database; (4) number of images used; and (5) performance measures including sensitivity, specificity and area under the curve (AUC).…”