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.

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.

scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

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.

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS

2014 ◽  
Vol 26 (03) ◽  
pp. 1450033 ◽  
Author(s):  
Maria Rizzi ◽  
Matteo D'Aloia
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Mammographic Screening ◽  
Image Features ◽  
General Validity ◽  
Artificial Neural Network Classifier ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Microcalcification Clusters

Computer aided detection and Diagnosis systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, a computer aided system for breast cancer diagnosis in mammographic images is presented. In particular, the method looks for microcalcification cluster occurrence and makes the diagnosis of the detected abnormality. The procedure first detects microcalcifications having a cluster pattern and then classifies the abnormalities as benign or malignant clusters. The method formulates the differentiation between malignant and benign microcalcification clusters as a supervised learning problem implementing an artificial neural network classifier. As input to the classifier, the procedure uses image features automatically extracted from the detected clusters. The seven features used are related both to the distribution of microcalcifications within cluster and to the uniformity of their shape. The performance of the implemented system is evaluated taking into account the accuracy of classifying clusters. The obtained results make this method able to operate as a "second opinion" helping radiologists during the routine clinical practice. Moreover, the implemented method has a general validity and can be used to detect and to classify microcalcification clusters independently from the acquisition equipment adopted during the mammographic screening.

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