scholarly journals Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

2019 ◽  
Vol 10 (3) ◽  
pp. 2071-2081
Author(s):  
Shobha Rani N ◽  
Chinmayi S Rao
Keyword(s):  
Breast Cancer ◽  
Cancer Control ◽  
Ensemble Methods ◽  
Breast Cancer Diagnosis ◽  
Gaussian Mixture ◽  
Gaussian Kernel ◽  
Linear Discriminant ◽  
Laplacian Filter ◽  
Detection And Diagnosis ◽  
Occurrence Matrix

Breast cancer is the second leading cause of death for women everywhere in the world. Since the reason behind the disease remains unknown, early detection and diagnosis is the key challenge for breast cancer control. In this work, mammogram images are initially subject to pre-processing using Laplacian filter for enhancement of tumour regions, Gaussian mixture model, Gaussian kernel FCM, Otsu global thresholding and FCM technique are employed for segmentation. Further, the efficiency of segmentation techniques is analyzed by classifying the samples into benign, malignant and healthy using Gray Level Co-occurrence Matrix (GLCM) features. Linear discriminant analysis classifier is used a combination based on which efficiency used for classification of mammograms. Ensemble methods are evaluated. The efficiency has resulted in better accuracy with the ensemble-based method. The experimentation is conducted in the mini MIAS database of mammograms, and the efficiency of the linear discriminant analyzer is found to be 89.19% for GKFCM, 83.78% with Otsu and 78.38% with FCM method with GLCM features.

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.

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.

A cost-effective computer-vision based breast cancer diagnosis

2021 ◽  
pp. 1-11
Author(s):  
Prabira Kumar Sethy ◽  
Chanki Pandey ◽  
Dr. Mohammad Rafique Khan ◽  
Santi Kumari Behera ◽  
K. Vijaykumar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Vision ◽  
Cancer Detection ◽  
Cost Effective ◽  
Breast Cancer Diagnosis ◽  
Breast Conserving Surgery ◽  
Breast Cancer Detection ◽  
World Health ◽  
Partial Mastectomy ◽  
Detection And Diagnosis

In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.

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.

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