Breast Cancer Image Classification: A Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Pooja Pathak ◽  
Anand Singh Jalal ◽  
Ritu Rai
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Treatment Options ◽  
Breast Cancer Diagnosis ◽  
Conventional Approach ◽  
Learning Networks ◽  
Cad System ◽  
Ultrasound Computed Tomography ◽  
Core Concepts ◽  
Magnetic Resonance Imaging Mri

Background: Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer such as mammogram, ultrasound, computed tomography, Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. Objective: This paper aims to cover the approaches used in CAD system for the detection of breast cancer. Method: In this paper, the methods used in CAD systems are categories in two classes: the conventional approach and artificial intelligence (AI) approach. The conventional approach covers the basic steps of image processing such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. Conclusion: This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.

Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography

2017 ◽  
pp. 925-941 ◽  
Author(s):  
Mohammed A. Osman ◽  
Ashraf Darwish ◽  
Ayman E. Khedr ◽  
Atef Z. Ghalwash ◽  
Aboul Ella Hassanien
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Fuzzy Clustering ◽  
Digital Mammography ◽  
Cancer Control ◽  
Breast Cancer Diagnosis ◽  
Cad System ◽  
Malignant Breast ◽  
Detection And Diagnosis ◽  
And Performance

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography

2018 ◽  
pp. 1968-1984
Author(s):  
Mohammed A. Osman ◽  
Ashraf Darwish ◽  
Ayman E. Khedr ◽  
Atef Z. Ghalwash ◽  
Aboul Ella Hassanien
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Fuzzy Clustering ◽  
Digital Mammography ◽  
Cancer Control ◽  
Breast Cancer Diagnosis ◽  
Cad System ◽  
Malignant Breast ◽  
Detection And Diagnosis ◽  
And Performance

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

scholarly journals A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Ensemble Classification ◽  
Classification Method ◽  
Ultrasound Images ◽  
Selective Ensemble ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

scholarly journals Impact of family history risk assessment on surgical decisions and imaging surveillance at breast cancer diagnosis

2020 ◽  
Vol 102 (8) ◽  
pp. 590-593
Author(s):  
S Jones ◽  
P Turton ◽  
R Achuthan
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Treatment Options ◽  
Breast Cancer Diagnosis ◽  
Team Meetings ◽  
Gene Testing ◽  
Nice Guidance ◽  
Imaging Surveillance ◽  
Personal Diagnosis

Introduction In June 2013, the National Institute for Health and Care Excellence (NICE) published guidance on the management of women with a family history (FH) of breast cancer (BC) and a personal diagnosis of BC. When diagnosed with BC, pressure of timely treatment takes priority and there is potential for a significant FH to be overlooked. This can affect treatment options and follow-up imaging (FUI) surveillance. Methods The practice in our breast unit was compared with the NICE guidance with regard to arranging appropriate FUI and referral to the genetics team for women diagnosed with BC with a FH of BC. Data were obtained retrospectively on 200 women with BC, identified from the breast multidisciplinary team meetings from January to March 2014. Initial audit showed poor compliance with recording of FH. A standardised history taking proforma was produced for clinic use. A re‐audit was conducted on a further 200 women between May and July 2016. Results In the initial audit, FH was taken in 151 women (76%) compared with 174 women (87%) in the re‐audit. Thirty-seven women (25%) were thought to be of moderate risk (MR) or high risk (HR) based on FH in the first audit. Re‐audit identified 35 women (20%) with MR or HR FH. Under half (43%) of the women of HR were referred to the genetics team initially; this increased to 70% in the second audit. While almost half (46%) of the women with MR or HR had inappropriate FUI in the initial audit, this fell to 11% in the re‐audit. Conclusions A proportion of women diagnosed with BC would fall into the MR or HR categories as defined in the NICE FH guidance. Inadequate recording of FH could result in inadequate FUI surveillance and in some cases missing the opportunity for a genetic referral to assess suitability for gene testing.

scholarly journals A quality enhanced preprocessing method for mammogram ROI extraction

2018 ◽  
Vol 7 (2.25) ◽  
pp. 133
Author(s):  
T R. Thamizhvani ◽  
Bincy Babu ◽  
A Josephin Arockia Dhivya ◽  
R J. Hemalatha ◽  
Josline Elsa Joseph ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Screening Test ◽  
Region Of Interest ◽  
Quality Enhancement ◽  
Cancer Death ◽  
Cad System ◽  
Computer Aided ◽  
Roi Extraction ◽  
Aided Diagnosis

Early detection of breast cancer is necessary because it is considered as one of the most common reason of cancer death among women. Nowadays, the basic screening test for detection of breast cancer is Mammography which con-sists of various artifacts. These artifacts leads to wrong results in detection of breast cancer. Therefore, Computer Aided Diagnosis (CAD) system mainly focus in removal of artifacts and mammogram quality enhancement. By this procedure, exact Region of Interest (ROI) can be obtained. This is a challenging procedure because detection of pecto-ral muscle and cancer region is difficult. Here a comparative study of different preprocessing and enhancement tech-niques are done by testing proposed system on mammogram mini-MIAS database. Result obtained shows that sug-gested system is efficient for CAD system.  

scholarly journals Functionalized nanoparticles with targeted antibody to enhance imaging of breast cancer in vivo

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Jesse S. Chen ◽  
Jingwen Chen ◽  
Somnath Bhattacharjee ◽  
Zhengyi Cao ◽  
Han Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Tumor Imaging ◽  
Growth Factor Receptor ◽  
Therapeutic Monitoring ◽  
Mouse Tumor ◽  
Her 2 ◽  
Epidermal Growth ◽  
Magnetic Resonance Imaging Mri

Abstract Background Targeted contrast nanoparticles for breast tumor imaging facilitates early detection and improves treatment efficacy of breast cancer. This manuscript reports the development of an epidermal growth factor receptor-2 (HER-2) specific, bi-modal, dendrimer conjugate to enhance computed tomography (CT) and magnetic resonance imaging (MRI) of HER-2-positive breast cancer. This material employs generation 5 poly(amidoamine) dendrimers, encapsulated gold nanoparticles, chelated gadolinium, and anti-human HER-2 antibody to produce the nanoparticle contrast agent. Results Testing in two mouse tumor models confirms this contrast agent’s ability to image HER-2 positive tumors. Intravenous injection of this nanoparticle in mice bearing HER-2 positive mammary tumors significantly enhances MRI signal intensity by ~ 20% and improves CT resolution and contrast by two-fold. Results by flow cytometry and confocal microscopy validate the specific targeting of the conjugate and its internalization in human HER-2 positive cells. Conclusion These results demonstrate that this nanoparticle conjugate can efficiently target and image HER-2 positive tumors in vivo and provide a basis for the development of this diagnostic tool for early detection, metastatic assessment and therapeutic monitoring of HER-2 positive cancers.

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.

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