scholarly journals Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset

2021 ◽  
Vol 113 ◽  
pp. 103656
Author(s):  
Rebecca Sawyer Lee ◽  
Jared A. Dunnmon ◽  
Ann He ◽  
Siyi Tang ◽  
Christopher Ré ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Clinical Application of Computer-Aided Diagnosis According to S-Detect in Category 4 Breast Lesions: Prospective Single-Center Study (Preprint)

2020 ◽  
Author(s):  
Pengfei Sun ◽  
Chen Chen ◽  
Weiqi Wang ◽  
Lei Liang ◽  
Dan Luo ◽  
...  
Keyword(s):  
Clinical Management ◽  
Diagnostic Performance ◽  
Breast Biopsy ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Breast Masses ◽  
Computer Aided ◽  
Conventional Ultrasound ◽  
Us Findings ◽  
Aided Diagnosis

BACKGROUND Computer-aided diagnosis (CAD) is a useful tool that can provide a reference for the differential diagnosis of benign and malignant breast lesion. Previous studies have demonstrated that CAD can improve the diagnostic performance. However, conventional ultrasound (US) combined with CAD were used to adjust the classification of category 4 lesions has been few assessed. OBJECTIVE The objective of our study was to evaluate the diagnosis performance of conventional ultrasound combined with a CAD system S-Detect in the category of BI-RADS 4 breast lesions. METHODS Between December 2018 and May 2020, we enrolled patients in this study who received conventional ultrasound and S-Detect before US-guided biopsy or surgical excision. The diagnostic performance was compared between US findings only and the combined use of US findings with S-Detect, which were correlated with pathology results. RESULTS A total of 98 patients (mean age 51.06 ±16.25 years, range 22-81) with 110 breast masses (mean size1.97±1.38cm, range0.6-8.5) were included in this study. Of the 110 breast masses, 64/110 (58.18%) were benign, 46/110 (41.82%) were malignant. Compared with conventional ultrasound, a significant increase in specificity (0% to 53.12%, P<.001), accuracy (41.81% to70.19%, P<.001) were noted, with no statistically significant decrease on sensitivity(100% to 95.65% ,P=.48). According to S-Detect-guided US BI-RADS re-classification, 30 out of 110 (27.27%) breast lesions underwent a correct change in clinical management, 74of 110 (67.27%) breast lesions underwent no change and 6 of 110 (5.45%) breast lesions underwent an incorrect change in clinical management. The biopsy rate decreased from 100% to 67.27 % (P<.001).Benign masses among subcategory 4a had higher rates of possibly benign assessment on S-Detect for the US only (60% to 0%, P<.001). CONCLUSIONS S-Detect can be used as an additional diagnostic tool to improve the specificity and accuracy in clinical practice. S-Detect have the potential to be used in downgrading benign masses misclassified as BI-RADS category 4 on US by radiologist, and may reduce unnecessary breast biopsy. CLINICALTRIAL none

scholarly journals Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

2018 ◽  
Vol 46 (9) ◽  
pp. 1419-1431 ◽  
Author(s):  
Gopichandh Danala ◽  
Bhavika Patel ◽  
Faranak Aghaei ◽  
Morteza Heidari ◽  
Jing Li ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Contrast Enhanced ◽  
Computer Aided ◽  
Aided Diagnosis

Comparison of Naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

2012 ◽  
Author(s):  
Theodore W. Cary ◽  
Alyssa Cwanger ◽  
Santosh S. Venkatesh ◽  
Emily F. Conant ◽  
Chandra M. Sehgal
Keyword(s):  
Logistic Regression ◽  
Ultrasound Imaging ◽  
Naive Bayes ◽  
Computer Aided Diagnosis ◽  
Naïve Bayes ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

A computer-aided diagnosis system for prediction of the probability of malignancy of breast masses on ultrasound images

2009 ◽  
Author(s):  
Jing Cui ◽  
Berkman Sahiner ◽  
Heang-ping Chan ◽  
Jiazheng Shi ◽  
Alexis Nees ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Said Boumaraf ◽  
Xiabi Liu ◽  
Chokri Ferkous ◽  
Xiaohong Ma
Keyword(s):  
Feature Selection ◽  
Predictive Value ◽  
Breast Imaging ◽  
Feature Selection Method ◽  
Computer Aided Diagnosis ◽  
Screening Mammography ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses

2005 ◽  
Vol 12 (4) ◽  
pp. 487-495 ◽  
Author(s):  
Jae H. Song ◽  
Santosh S. Venkatesh ◽  
Emily A. Conant ◽  
Peter H. Arger ◽  
Chandra M. Sehgal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Comparative Analysis ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Computer Aided ◽  
Artificial Neural ◽  
Aided Diagnosis

Breast Masses: Computer-aided Diagnosis with Serial Mammograms

Radiology ◽  
2006 ◽  
Vol 240 (2) ◽  
pp. 343-356 ◽  
Author(s):  
Lubomir Hadjiiski ◽  
Berkman Sahiner ◽  
Mark A. Helvie ◽  
Heang-Ping Chan ◽  
Marilyn A. Roubidoux ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Novel Computer-Aided Diagnosis Algorithms on Ultrasound Image: Effects on Solid Breast Masses Discrimination

2009 ◽  
Vol 23 (5) ◽  
pp. 581-591 ◽  
Author(s):  
Ying Wang ◽  
Hong Wang ◽  
Yanhui Guo ◽  
Chunping Ning ◽  
Bo Liu ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis
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