scholarly journals A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Idil Isikli Esener ◽  
Semih Ergin ◽  
Tolga Yuksel
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Classification Scheme ◽  
Region Of Interest ◽  
Breast Cancer Diagnosis ◽  
Histogram Equalization ◽  
Majority Voting ◽  
Nonlocal Means ◽  
Cad System ◽  
Multistage Classification

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

scholarly journals Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Meenakshi M. Pawar ◽  
Sanjay N. Talbar ◽  
Akshay Dudhane
Keyword(s):  
Breast Cancer ◽  
Region Of Interest ◽  
Breast Cancer Diagnosis ◽  
Local Binary Patterns ◽  
Self Organizing Map ◽  
Entropy Maximization ◽  
Cad System ◽  
Som Clustering ◽  
Artificial Neural Network Ann ◽  
Feature Mining

Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.

scholarly journals A Research on Computer Aided Detection System for Women Breast Cancer Diagnosis from Digital Mammographic Images

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1008-1014
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Death Rate ◽  
Computational Cost ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Detection ◽  
Cad Systems ◽  
Cad System ◽  
Mammographic Images ◽  
Computer Aided

The Women breast cancer is the most critical cancer that are found in women. Its the second important cause of death in the world. Breast cancer has been ranked number one cancer in Indian females with rates occurrence of 25.8 per 1,00,000 females and death rate 12.7 among 1,00,000. Generally breast cancer is a malignant tumor that begins in the cells of the breast and eventually it spreads to the surrounding tissues. Early detection and diagnosis can reduce the mortality rate. Radiologist misdiagnosis the disease due to technical issues such as imaging quality and human error. Radiologists can improve the performance of Computer Aided Detection/Diagnosis (CAD) systems to finding and discriminating between the normal and abnormal tissues. Breast cancer diagnosis can applied are applied recent CAD systems on imaging modalities such as mammogram, ultrasound, MRI and biopsy histopathological images. CAD system have four stages for diagnosis which are pre-processing, segmentation, Feature Extraction and Classification. CAD system are developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. This paper focus on the survey of CAD system to detect women breast cancer disease from the digital mammographic images to achieve high accuracy and low computational cost.

scholarly journals Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

2020 ◽  
Vol 10 (3) ◽  
pp. 2336 ◽  
Author(s):  
Susama Bagchi ◽  
Kim Gaik Tay ◽  
Audrey Huong ◽  
Sanjoy Kumar Debnath
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Imaging ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Computer Aided Detection ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.

Physical Activity and Survival Subsequent to Metastatic Breast Cancer Diagnosis

2010 ◽  
Author(s):  
Susan Sharp ◽  
Ashleigh Golden ◽  
Cheryl Koopman ◽  
Eric Neri ◽  
David Spiegel
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Metastatic Breast Cancer ◽  
Cancer Diagnosis ◽  
Metastatic Breast ◽  
Breast Cancer Diagnosis
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