scholarly journals Breast Cancer Diagnosis by using k-Nearest Neighbor with Different Distances and Classification Rules

2013 ◽  
Vol 62 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Seyyid AhmedMedjahed ◽  
Tamazouzt Ait Saadi ◽  
Abdelkader Benyettou
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Nearest Neighbor ◽  
Breast Cancer Diagnosis ◽  
Classification Rules ◽  
K Nearest Neighbor

scholarly journals Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 165 ◽  
Author(s):  
Ragab ◽  
Sharkas ◽  
Attallah
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Area Under The Curve ◽  
Breast Cancer Diagnosis ◽  
Pectoral Muscle ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
K Nearest Neighbor ◽  
Cad System ◽  
Analysis Society

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100 %, which was achieved with k‐NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.

scholarly journals Mobile Application for Breast Cancer Diagnosis using Morphological Associative Memories Implemented on a Cloud Platform

2019 ◽  
Vol 9 (2) ◽  
pp. 3763-3769
Keyword(s):  
Breast Cancer ◽  
Information System ◽  
Hybrid Model ◽  
Nearest Neighbor ◽  
Binary Classification ◽  
False Positive Rate ◽  
Breast Cancer Diagnosis ◽  
Associative Memories ◽  
K Nearest Neighbor ◽  
Mobile Information

The astounding advances that have been observed in mobile device technologies and their underlying algorithms have prompted a worldwide surge in attention to their capabilities and potential for improving different human activities. The present work is framed by the academic cooperation process between Mexico and Saudi Arabia; it consists of the description of the design and development of a mobile information system aimed at performing diagnosis and verification of breast cancer using an application for mobile devices. The problem to be solved is represented as a binary classification problem between healthy patients and people that have been confirmed as control cases. The classification algorithm is a hybrid model, consisting of Morphological Associative Memories and the k-Nearest Neighbor classifier. The hybrid model improves upon the performance of its components. The proposed model was implemented on a cloud computing platform in order to optimize the response time for the diagnosis. A comparative study of our proposal and the state of the art shows that the proposed mobile information system has a high classification performance as well as a low false positive rate.

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

The significance of patient’s knowledge and behavior after breast cancer diagnosis

2019 ◽  
Vol 3 (48) ◽  
pp. 7
Author(s):  
Alina Oana Rusu-Moldovan ◽  
Maria Iuliana Gruia ◽  
Dan Mihu
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
And Behavior
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