scholarly journals Intelligent 3D Analysis for Detection and Classification of Breast Cancer

2019 ◽  
Vol 3 (02) ◽  
pp. 96-103
Author(s):  
Suzani Mohamad samuri ◽  
Try Viananda Nova Megariani
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Control ◽  
Public Health Problem ◽  
Breast Cancer Diagnosis ◽  
Cancer Prognosis ◽  
3D Analysis ◽  
Mass Detection ◽  
Modelling Framework ◽  
Significant Public Health

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Breast cancer computer aided diagnosis (CAD) systems can provide such help and they are important and necessary for breast cancer control. Micro calcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This research presents algorithms for building a classification system or CAD, especially to obtain the different characteristics of mass and micro calcification using association technique based on classification. Starting with an individual-specific deformable of 3D breast model, this modelling framework will be useful for tracking visible tumors between mammogram images, as well as for registering breast images taken from different imaging modalities. From the results, the classifier developed able to perform well by successfully classifying the cancer and non-cancer (normal) images with the accuracy of 97%. Apart from that, by applying color map to the final results of segmentation provides a more interesting display of information and gives more direction to the purpose of image processing, which distinguishes between cancerous and non-cancerous tissues.

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.

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.

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.

Breast Cancer in Latin America: A Map of the Disease in the Region

2018 ◽  
pp. 451-456 ◽  
Author(s):  
Eduardo Cazap
Keyword(s):  
Breast Cancer ◽  
Health Care ◽  
Latin America ◽  
Urban Areas ◽  
Access To Health Care ◽  
Cancer Control ◽  
Public Health Problem ◽  
The United States ◽  
High Morbidity ◽  
Patient Organizations

In the next few decades, breast cancer will become a leading global public health problem as it increases disproportionately in low- and middle-income countries. Disparities are clear when comparisons are made with rates in Europe and the United States, but they also exist between the countries of the region or even within the same country in Latin America. Large cities or urban areas have better access and resource availability than small towns or remote zones. This article presents the status of the disease across 12 years with data obtained through three studies performed in 2006, 2010, and 2013 and based on surveys, reviews of literature, patient organizations, and public databases. The first study provided a general picture of breast cancer control in the region (Latin America); the second compared expert perceptions with medical care standards; and the third was a review of literature and public databases together with surveys of breast cancer experts and patient organizations. We conclude that breast cancer is the most frequent cancer and kills more women than any other cancer; we also suggest that aging is the principal risk factor, which will drive the incidence to epidemic levels as a result of demographic transition in Latin America. The economic burden also is large and can be clearly observed: in countries that today allocate insufficient resources, women go undiagnosed or uncared for or receive treatment with suboptimal therapies, all of which results in high morbidity and the associated societal costs. The vast inequities in access to health care in countries translates into unequal results in outcomes. National cancer control plans are the fundamental building block to an organized governance, financing, and delivery of health care for breast cancer.

scholarly journals Challenges, Opportunities, and Priorities for Advancing Breast Cancer Control in Zambia: A Consultative Meeting on Breast Cancer Control

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Anna Cabanes ◽  
Sharon Kapambwe ◽  
Susan Citonje-Msadabwe ◽  
Groesbeck P. Parham ◽  
Kennedy Lishimpi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Breast Cancer Mortality ◽  
Cancer Control ◽  
Control Program ◽  
Cervical Cancer Prevention ◽  
National Guidelines ◽  
National Breast ◽  
National Priority ◽  
Breast Cancer Control

In 2016, the Zambian government made cancer control a national priority and released a National Cancer Control Strategic Plan for 2016 to 2021, which focuses on malignancies of the breast, cervix, and prostate, and retinoblastoma. The plan calls for a collective reduction in the cancer burden by 50%. In support of this vision, Susan G. Komen sponsored a consultative meeting in Lusaka, Zambia, in September 2017 to bring together the country’s main breast cancer stakeholders and identify opportunities to improve breast cancer control. The recommendations generated during the discussions are presented. There was general agreement that the first step toward breast cancer mortality reduction should consist of implementation of early detection service platforms focused on women who are symptomatic. Participants also agreed that the management of all components of the national breast cancer control program should be integrated and led by the Ministry of Health. As much as possible, early detection and treatment services presently offered by the Cervical Cancer Prevention Program of Zambia and Cancer Diseases Hospital should be leveraged. Efforts are under way through multiple stakeholders to implement the following recommendations: development of national guidelines for the early diagnosis of breast cancer, training of breast surgeons, implementation of early detection and surgical treatment service platforms at the district-hospital level, and epidemiologic research, including the improvement of electronic recording mechanisms.

scholarly journals Correlation between Breast Self-Examination Practices and Demographic Characteristics, Risk Factors and Clinical Stage of Breast Cancer among Iraqi Patients

2019 ◽  
Vol 7 (19) ◽  
pp. 3216-3220
Author(s):  
Ahmad S. A. Al-Gburi ◽  
Nada A. S. Alwan
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Family History ◽  
Early Detection ◽  
Clinical Stage ◽  
Cancer Control ◽  
Demographic Characteristics ◽  
Breast Self Examination ◽  
History Of ◽  
Self Examination

BACKGROUND: Breast Cancer (BC) is the most common cancer and the leading cause of cancer death among women globally. The disease can be cured with limited resources if detected early. Breast self-examination (BSE) is considered a cost-effective feasible approach for early detection of that cancer in developing countries. AIM: To determine the correlation between BSE performance and demographic characteristics, risk factors and clinical stage of BC among Iraqi patients. METHODS: This retrospective study included a total of 409 female patients diagnosed with BC at the Referral Training Center for Early Detection of Breast Cancer and the National Cancer Research Center in Baghdad. The studied variables included the age of the patient, occupation, marital and educational status, parity, history of lactation, contraceptive pill intake, family history of cancer and the clinical stage of the disease. RESULTS: Our findings revealed that the most important predictors for practicing BSE was family history of BC or any other cancers (OR = 3.87, P = 0.018) followed by being a governmental employee (OR = 1.87, P = 0.024), history of contraceptive use (OR = 1.80, P = 0.011) and the high level of education (OR = 1.73, P = 0.004). On the other hand, there was no significant correlation between the practice of BSE and the BC stage at the time of presentation. CONCLUSION: There is a relatively poor practice of BSE among Iraqi patients diagnosed with BC. It is mandatory to foster the national cancer control strategies that focus on raising the level of awareness among the community through public education as a major approach to the early detection of cancer in Iraq.

Fuzzy Logic for Breast Cancer Diagnosis Using Medical Thermogram Images

2017 ◽  
pp. 354-388 ◽  
Author(s):  
Surekha Kamath
Keyword(s):  
Breast Cancer ◽  
Fuzzy Logic ◽  
Early Detection ◽  
Early Diagnosis ◽  
Cause Of Death ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Detection Technique ◽  
Breast Diseases ◽  
Benign Neoplasms

In this chapter, how medical thermography can be utilized as early detection technique for breast cancer with fuzzy logic is explained. Breast cancer is the leading cause of death among women. This fact justifies researches to reach early diagnosis, improving patients' life expectancies. Moreover, there are other pathologies, such as cysts and benign neoplasms, that deserve investigation. In the last ten years, the infrared thermography has shown to be a promising technique to early diagnosis of breast pathologies. Works on this subject presented results that justify the thermography as a complementary exam to detect breast diseases. Various algorithms that can be utilized for Breast Cancer diagnosis utilizing medical thermography are listed and also the advantages of medical thermography over other imaging modalities is given.

Reconstruction of EIT Images Using Fish School Search and Non-Blind Search

2021 ◽  
Vol 10 (1) ◽  
pp. 89-103
Author(s):  
Valter Augusto de Freitas Barbosa ◽  
David Edson Ribeiro ◽  
Clarisse Lins de Lima ◽  
Maíra Araújo de Santana ◽  
Ricardo Emmanuel de Souza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Ionizing Radiation ◽  
Reconstruction Algorithm ◽  
Breast Cancer Diagnosis ◽  
Element Mesh ◽  
Impedance Tomography ◽  
Fish School ◽  
Reconstruction Methods ◽  
Blind Search

Breast cancer is the most common type of cancer among women, affecting 2.1 million women per year worldwide. The best strategy for decreasing disease morbidity and mortality is early detection. Mammography is the most used exam for the diagnosis of breast cancer. However, this technique uses ionizing radiation and causes discomfort to the patient. One promising technique that can be used for early detection of breast cancer is electrical impedance tomography (EIT), which is an imaging technique free of ionizing radiation. Yet, its images still have low resolution, making it difficult to use in breast cancer diagnosis. Thus, the development of new reconstruction methods aiming better resolution is necessary. This work evaluates the performance of the reconstruction algorithm based on fish school search with non-blind search in a 3,190 finite element mesh.

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