Automatic Mammogram image Breast Abnormality Detection and localization based on the combination of k-means and Genetic algorithms methods

2020 ◽  
Vol 9 (1.5) ◽  
pp. 76-83
Author(s):  
Hamid El malali, ,Abdelhadi Assir
Keyword(s):  
Genetic Algorithms ◽  
Abnormality Detection ◽  
Breast Abnormality ◽  
Mammogram Image ◽  
Detection And Localization

scholarly journals A new approach for breast abnormality detection based on thermography

2018 ◽  
Vol 2 (3) ◽  
pp. 245-254 ◽  
Author(s):  
Chebbah Nabil Karim ◽  
Ouslim Mohamed ◽  
Temmar Ryad
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormality Detection ◽  
Public Database ◽  
New Approach ◽  
Image Processing Techniques ◽  
Breast Abnormality ◽  
Processing Techniques

Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method  showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates.

Breast abnormality detection using combined texture and vascular features

2019 ◽  
Vol 18 (2) ◽  
pp. 140 ◽  
Author(s):  
Sourav Pramanik ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri
Keyword(s):  
Abnormality Detection ◽  
Breast Abnormality
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