False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer

2018 ◽  
Vol 42 (8) ◽  
Author(s):  
Jonathan Hernández-Capistrán ◽  
Jorge F. Martínez-Carballido ◽  
Roberto Rosas-Romero
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
False Positive ◽  
False Positive Reduction ◽  
Microcalcification Detection

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

False-positive diagnosis of breast cancer by diffused optical tomography with ultrasound

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22085-e22085
Author(s):  
X. Wang ◽  
Y. Li ◽  
X. Cao
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Diagnostic Tool ◽  
False Positive ◽  
Breast Tumor ◽  
Optical Tomography ◽  
False Negative ◽  
False Negative Rate ◽  
Breast Disease ◽  
Study Results

e22085 Background: Breast cancer is one of the most common cancer in women. Early detection, early diagnosis and early treatment play key role in fighting against breast cancer. OPTIMUS system is a system of diffused optical tomography with ultrasound. It provides dual modality images for early diagnosis of breast cancer. The aim of this study was to evaluate the OPTIMUS system on diagnosis of breast disease. Methods: OPTIMUS system was applied to 160 breast tumor patients. All patients had received surgical treatment and had definite pathological diagnosis. OPTIMUS system was evaluated as diagnostic tool of breast tumor in this study. Results: There were 42 cases diagnosed as benign breast disease and 118 cases diagnosed as breast cancer by OPTIMUS system. Pathology confirmed 60 cases of benign disease and 100 cases of breast cancer. False positive rate of breast cancer was 30% (18/60). False negative rate of breast cancer was 0% (0/100). The pathology of false positive cases was mild and severe papillomatosis (6/18), non-typical hyperplasia (4/18), chronic inflammation (3/18), fibroadenoma (3/18) and fat necrosis (2/18). Papillomatosis and non-typical hyperplasia are precancerous lesions and often difficult for clinical diagnosis. In this study the false positive diagnostic rate of mammography and ultrasonography is 13% and 11.1% respectively. Conclusions: OPTIMUS system is a non- invasive and highly effective diagnostic tool for breast disease. Its sensitivity is reached to 100% and specificity is about 70% on the diagnosis of breast cancer. OPTIMUS system could be used as assistant diagnostic tool for breast tumor. No significant financial relationships to disclose.

scholarly journals Breast Segmentation and Probable Region Identification for Breast Cancer using DL-CNN

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Keyword(s):  
Breast Cancer ◽  
False Positive ◽  
Research Work ◽  
Region Of Interest ◽  
Layered Architecture ◽  
Proposed Model ◽  
False Positive Reduction ◽  
Breast Segmentation ◽  
Novel Method ◽  
Result Analysis

Mammography is one of the key method used for detecting the breast cancer, several researcher has proposed the detection and segmentation method, however absolute solution has not developed till now and they have certain limitation and still it is one of the major challenge for finding the region in masses. Hence in this research work we have developed and design a novel method named as DL-CNN (Dual Layered) architecture CNN. The main intention of the model is segmentation and probable region identification. DL-CNN is based on the Convolution Neural Network. It has two layer first layer is applied for identifying the probable region whereas the second layer is used for segmentation and minimizing the false positive Reduction. In order to evaluate the DL-CNN algorithm by using the In Breast Dataset. Moreover the proposed model is compared against the various model in terms of ROI(Region of Interest), Dice_Index and False positive per Image. Result analysis shows that our model outperforms the existing

scholarly journals Subsequent attendance in a breast cancer screening program after a false-positive result in the Local Health Authority of Bologna (Italy)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Squillace ◽  
Lorenzo Pizzi ◽  
Flavia Rallo ◽  
Carmen Bazzani ◽  
Gianni Saguatti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Positive Result ◽  
Breast Cancer Screening ◽  
Health Authority ◽  
False Positive ◽  
Screening Program ◽  
Local Health Authority ◽  
Local Health ◽  
False Positive Result

AbstractWe conducted a cross-sectional study to assess the likelihood of returning for routine breast cancer screening among women who have experienced a false-positive result (FPR) and to describe the possible individual and organizational factors that could influence subsequent attendance to the screening program. Several information were collected on demographic and clinical characteristics data. Electronic data from 2014 to 2016 related to breast screening program of the Local Health Authority (LHA) of Bologna (Italy) of women between 45 and 74 years old were reviewed. A total of 4847 women experienced an FPR during mammographic screening and were recalled to subsequent round; 80.2% adhered to the screening. Mean age was 54.2 ± 8.4 years old. Women resulted to be less likely to adhere to screening if they were not-Italian (p = 0.001), if they lived in the Bologna district (p < 0.001), if they had to wait more than 5 days from II level test to end of diagnostic procedures (p = 0.001), if the diagnostic tests were performed in a hospital with the less volume of activity and higher recall rate (RR) (p < 0.001) and if they had no previous participation to screening tests (p < 0.001). Our results are consistent with previous studies, and encourages the implementation and innovation of the organizational characteristics for breast cancer screening. The success of screening programs requires an efficient indicators monitoring strategy to develop and evaluate continuous improvement processes.

scholarly journals Advanced and futuristic approaches for breast cancer diagnosis

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jayanti Mishra ◽  
Bhumika Kumar ◽  
Monika Targhotra ◽  
P. K. Sahoo
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Diagnostic Techniques ◽  
Main Body ◽  
Detection Techniques ◽  
Initial Stage ◽  
Common Causes ◽  
Delay In Treatment

Abstract Background Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage. Main body Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool. Conclusion It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.

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