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Paper Information

Journal:   MIDDLE EAST JOURNAL OF CANCER   JULY 2016 , Volume 7 , Number 3; Page(s) 113 To 124.
 
Paper: 

EARLY BREAST CANCER DETECTION IN THERMOGRAM IMAGES USING ADABOOST CLASSIFIER AND FUZZY C-MEANS CLUSTERING ALGORITHM

 
 
Author(s):  LASHKARI AMIR EHSAN*, FIROUZMAND MOHAMMAD
 
* DEPARTMENT OF BIO-MEDICAL ENGINEERING INSTITUTE OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY, IRANIAN RESEARCH ORGANIZATION FOR SCIENCE AND TECHNOLOGY (IROST), TEHRAN, IRAN
 
Abstract: 

Background: In this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer.
Methods: First, we segmented the images and determined the region of interest. Then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence matrix based features were extracted from the segmented right and left breasts. To achieve the best features, feature selection methods such as minimum redundancy and maximum relevance, sequential forward selection, sequential backward selection, sequential floating forward selection, sequential floating backward selection, and genetic algorithm were used. Contrast, energy, Euler number, and kurtosis were marked as effective features.
Results: The selected features were evaluated by fuzzy C-means clustering as the unsupervised method and compared with the AdaBoost supervised classifier which has been previously studied. As reported, fuzzy C-means clustering with a mean accuracy of 75% can be suitable for unsupervised techniques.
Conclusion: Fuzzy C-means clustering can be a suitable unsupervised technique to determine suspicious areas in thermal images compared to AdaBoost as the supervised technique with a mean accuracy of 88%.

 
Keyword(s): BREAST CANCER, BREAST THERMOGRAPHY, THERMOGRAM, FEATURE SELECTION, CLASSIFICATION, TH
 
 
References: 
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Click to Cite.
APA: Copy

LASHKARI, A., & FIROUZMAND, M. (2016). EARLY BREAST CANCER DETECTION IN THERMOGRAM IMAGES USING ADABOOST CLASSIFIER AND FUZZY C-MEANS CLUSTERING ALGORITHM. MIDDLE EAST JOURNAL OF CANCER, 7(3), 113-124. https://www.sid.ir/en/journal/ViewPaper.aspx?id=507157



Vancouver: Copy

LASHKARI AMIR EHSAN, FIROUZMAND MOHAMMAD. EARLY BREAST CANCER DETECTION IN THERMOGRAM IMAGES USING ADABOOST CLASSIFIER AND FUZZY C-MEANS CLUSTERING ALGORITHM. MIDDLE EAST JOURNAL OF CANCER. 2016 [cited 2021May13];7(3):113-124. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=507157



IEEE: Copy

LASHKARI, A., FIROUZMAND, M., 2016. EARLY BREAST CANCER DETECTION IN THERMOGRAM IMAGES USING ADABOOST CLASSIFIER AND FUZZY C-MEANS CLUSTERING ALGORITHM. MIDDLE EAST JOURNAL OF CANCER, [online] 7(3), pp.113-124. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=507157.



 
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