scholarly journals A cad system for improving classification performance in breast cancer detection

2018 ◽  
Vol 7 (2.25) ◽  
pp. 89
Author(s):  
Bincy Babu ◽  
A Josephin Arockia Dhivya ◽  
R Chandrasekaran ◽  
T R. Thamizhvani ◽  
R J.Hemalatha
Keyword(s):  
Breast Cancer ◽  
Computational Study ◽  
Breast Cancer Mortality ◽  
Extraction Methods ◽  
Classification Performance ◽  
Screening Mammography ◽  
Training Data ◽  
Svm Classifier ◽  
Cad System ◽  
Key Factor

Early detection is a key factor in reducing breast cancer mortality rate. Research works in the area of mammography plays an important role in identification of calcification clusters and detection of breast cancer.The purpose of proposed research is to find the best combination of feature extraction algorithm to classify mammogram into benign and malignant. It includes Marker Controlled Watershed Segmentation Technique (MCWS), feature set extraction methods and SVM classifier algorithm. The GLCM, GLRLM and first order texture descriptors are used to describe the calcification clusters.The standard inputs such as normal and abnormal breast images for the proposed system are taken from Digital Database for Screening Mammography (DDSM). The computational study showed that combination of all the three features descriptors provide better classification result with 97% accuracy and it ensures improved the CAD system performance for small training data sets compared to existing techniques.  

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

A Computer-Assisted Diagnostic (CAD) of Screening Mammography to Detect Breast Cancer Without a Surgical Biopsy

2019 ◽  
Vol 11 (4) ◽  
pp. 31-49 ◽  
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Breast Cancer ◽  
Screening Mammography ◽  
Computer Assisted ◽  
Surgical Biopsy ◽  
Major Health Problem ◽  
Major Health ◽  
Cad System ◽  
Digital Database ◽  
The Social ◽  
N Gram

Breast cancer has become a major health problem in the world over the past 50 years and its incidence has increased in recent years. It accounts for 33% of all cancer cases, and 60% of new cases of breast cancer occur in women aged 50 to 74 years. In this work we have proposed a computer-assisted diagnostic (CAD) system that can predict whether a woman has cancer or not by analyzing her mammogram automatically without passing through a biopsy stage. The screening mammogram will be vectorized using the n-gram pixel representation. After the vectors obtained will be classified into one of the classes—with cancer or without cancer—using the social elephant algorithm. The experimentation using the digital database for screening mammography (DDSM) and validation measures—f-measure entropy recall, accuracy, specificity, RCT, ROC, AUC—show clearly the effectiveness and the superiority of our proposed bioinspired technique compared to others techniques existed in the literature such as naïve bayes, Knearest neighbours, and decision tree c4.5. The goal is to help radiologists with early detection to reduce the mortality rate among women with breast cancer.

Continuation of Annual Screening Mammography and Breast Cancer Mortality in Women Older Than 70 Years

2020 ◽  
Vol 172 (6) ◽  
pp. 381 ◽  
Author(s):  
Xabier García-Albéniz ◽  
Miguel A. Hernán ◽  
Roger W. Logan ◽  
Mary Price ◽  
Katrina Armstrong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Mortality ◽  
Breast Cancer Mortality ◽  
Screening Mammography ◽  
Annual Screening

scholarly journals Service screening mammography reduces breast cancer mortality among elderly women in Turku

2006 ◽  
Vol 13 (1) ◽  
pp. 34-40 ◽  
Author(s):  
I Parvinen ◽  
H Helenius ◽  
L Pylkkänen ◽  
A Anttila ◽  
P Immonen-Räihä ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Mortality ◽  
Elderly Women ◽  
Breast Cancer Mortality ◽  
Screening Mammography

scholarly journals Impact of screening mammography on breast cancer mortality

2015 ◽  
Vol 138 (8) ◽  
pp. 2003-2012 ◽  
Author(s):  
Archie Bleyer ◽  
Cornelia Baines ◽  
Anthony B. Miller
Keyword(s):  
Breast Cancer ◽  
Cancer Mortality ◽  
Breast Cancer Mortality ◽  
Screening Mammography

Original Paper: Contribution of clinical breast examination to mammography screening in the early detection of breast cancer

2003 ◽  
Vol 10 (1) ◽  
pp. 16-21 ◽  
Author(s):  
C Bancej ◽  
K Decker ◽  
A Chiarelli ◽  
M Harrison ◽  
D Turner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Breast Cancer Mortality ◽  
Clinical Breast Examination ◽  
Screening Mammography ◽  
Detection Rates ◽  
Breast Examination ◽  
Mode Of Detection ◽  
Clinical Breast ◽  
Interim Measures

Objectives: As the benefit of clinical breast examination (CBE) over that of screening mammography alone in reducing breast cancer mortality is uncertain, it is informative to monitor its contribution to interim measures of effectiveness of a screening programme. Here, the contribution of CBE to screening mammography in the early detection of breast cancer was evaluated. Setting: Four Canadian organised breast cancer screening programmes. Methods: Women aged 50-69 receiving dual screening (CBE and mammography) (n=300,303) between 1996 and 1998 were followed up between screen and diagnosis. Outcomes assessed by mode of detection (CBE alone, mammography alone, or both CBE and mammography) included referral rate, positive predictive value, pathological features of tumours (size, nodal status, morphology), and cancer detection rates overall and for small cancers (≤10 mm or node-negative). Heterogeneity in findings across programmes was also assessed. Results: On first versus subsequent screen, CBE alone resulted in 28.5-36.7% of referrals, and 4.6-5.9% of cancers compared with 52.6-60.1% of referrals and 60.0-64.3% of cancers for mammography alone. Among cancers detected by CBE, 83.6-88.6% were also detected by mammography, whereas for mammographically detected cancers only 31.7-37.2% were also detected by CBE. On average, CBE increased the rate of detection of small invasive cancers by 2-6% over rates if mammography was the sole detection method. Without CBE, programmes would be missing three cancers for every 10,000 screens and 3-10 small invasive cancers in every 100,000 screens. Conclusions: Inclusion of CBE in an organised programme contributes minimally to early detection.

The effect of mammography screening regimen on incidence-based breast cancer mortality

2018 ◽  
Vol 25 (4) ◽  
pp. 197-204 ◽  
Author(s):  
Martin J Yaffe ◽  
Nicole Mittmann ◽  
Oguzhan Alagoz ◽  
Amy Trentham-Dietz ◽  
Anna NA Tosteson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Mortality ◽  
Breast Cancer Incidence ◽  
Temporal Distribution ◽  
Screening Mammography ◽  
Breast Cancers ◽  
Breast Cancer Death ◽  
Life Years ◽  
Life Years Lost ◽  
The Mean

Objectives Incidence-based mortality quantifies the distribution of cancer deaths and life-years lost, according to age at detection. We investigated the temporal distribution of the disease burden, and the effect of starting and stopping ages and interval between screening mammography examinations, on incidence-based mortality. Methods Incidence-based mortality was estimated using an established breast cancer simulation model, adapted and validated to simulate breast cancer incidence, screening performance, and delivery of therapies in Canada. Ten strategies were examined, with varying starting age (40 or 50), stopping age (69 or 74), and interval (1, 2, 3 years), and “No Screening.” Life-years lost were computed as the difference between model predicted time of breast cancer death and that estimated from life tables. Results Without screening, 70% of the burden in terms of breast cancer deaths extends between ages 45 and 75. The mean of the distribution of ages of detection of breast cancers that will be fatal in an unscreened population is 61.8 years, while the mean age of detection weighted by the number of life-years lost is 55, a downward shift of 6.8 years. Similarly, the mean age of detection for the distribution of life-years gained through screening is lower than that for breast cancer deaths averted. Conclusion Incidence-based mortality predictions from modeling elucidate the age dependence of the breast cancer burden and can provide guidance for optimizing the timing of screening regimens to achieve maximal impact. Of the regimens studied, the greatest lifesaving effect was achieved with annual screening beginning at age 40.

scholarly journals Breast cancer mortality and screening mammography in New Zealand: Incidence-based and aggregate analyses

2018 ◽  
Vol 26 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Richard Taylor ◽  
Marli Gregory ◽  
Kerry Sexton ◽  
Jessica Wharton ◽  
Nisha Sharma ◽  
...  
Keyword(s):  
Breast Cancer ◽  
New Zealand ◽  
Cancer Mortality ◽  
Mammography Screening ◽  
Breast Cancer Mortality ◽  
Primary Treatment ◽  
Screening Mammography ◽  
Randomized Controlled ◽  
Before And After ◽  
Incident Cases

Objective To investigate trends in breast cancer mortality in New Zealand women, to corroborate or negate a causal association with service screening mammography. Method Cumulated mortality rates from breast cancer deaths individually linked to incident cases diagnosed before and after screening commencement were compared, in women aged 50–64 (from 2001) and aged 45–49 and 65–69 (from 2006). Trends and differences in aggregate invasive breast cancer mortality (1975–2013) were assessed in relation to introduction of mammography screening targeting women aged 50–64 and 45–69. Joinpoint analysis was also undertaken. Results The reduction in incidence-based cumulated breast cancer mortality before and after the introduction of screening was −15% (p = 0.006) for women aged 45–69, and 17% (p = 0.005) for those aged 50–64. Aggregate mortality declined by −34% (2005–13 compared with 1992–98) in the age group 50–64, and by –28% among women aged 45–49 and –25% among women aged 65–74. For women aged 50–64 the 2-joinpoint model shows a 1990 turning point, from prior rising mortality to a mean −1.8% decline per annum, coinciding with improvements in primary treatment of breast cancer; and a steepening of the decline (−3.0% p.a.) from the late 1990s, coinciding with the introduction of service mammography screening. Conclusion Breast cancer mortality declines occurring since the advent of screening mammography in New Zealand are consistent with other incidence-based and aggregate studies of screening mammography in populations, individual-based cohort studies, and randomized controlled trials.

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