Discrimination of benign and malignant solid breast masses using deep residual learning-based bimodal computer-aided diagnosis system

2022 ◽  
Vol 73 ◽  
pp. 103453
Author(s):  
Zahra Assari ◽  
Ali Mahloojifar ◽  
Nasrin Ahmadinejad
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Diagnosis System ◽  
Residual Learning ◽  
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 computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound

Medicine ◽  
2019 ◽  
Vol 98 (3) ◽  
pp. e14146 ◽  
Author(s):  
Hee Jeong Park ◽  
Sun Mi Kim ◽  
Bo La Yun ◽  
Mijung Jang ◽  
Bohyoung Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Particle swarm optimized computer aided diagnosis system for classification of breast masses

2017 ◽  
Vol 32 (4) ◽  
pp. 2819-2828 ◽  
Author(s):  
Stephan Punitha ◽  
Subban Ravi ◽  
M. Anousouya Devi ◽  
Jothimani Vaishnavi
Keyword(s):  
Particle Swarm ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Analysis of Ultrasonographic Findings of Breast Masses by the Nagasawa Computer-Aided Diagnosis System

2006 ◽  
pp. 190-195
Author(s):  
Setsuko Kaoku ◽  
Yasuyuki Kato ◽  
Tsutomu Takashima ◽  
Yoshinari Ogawa ◽  
Yasuhisa Fujimoto ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Diagnosis System ◽  
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

An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

2021 ◽  
Vol 69 ◽  
pp. 102914
Author(s):  
Raouia Mokni ◽  
Norhene Gargouri ◽  
Alima Damak ◽  
Dorra Sellami ◽  
Wiem Feki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Multimodal Fusion ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Aided ◽  
Aided Diagnosis
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