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dc.contributor.authorMishra, Vartika-
dc.contributor.authorSingh, Yamini-
dc.contributor.authorRath, Santanu Kumar-
dc.identifier.citation5th International Conference for Convergence in Technology (I2CT 2019), Pune, India, 29-31 March 2019en_US
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractPresence of tumors in breasts have lead to possibility of occurrence of cancer in a global level. It’s diagnosis is one of the challenging tasks. Researchers have come across a technique named thermography, which overcomes the drawbacks of a conventional technique i.e., mammography. In thermography, the early diagnosis of the breast cancer is carried out by implementing an analytical infrared thermal imaging techniques. This work focuses on different features-based machine learning techniques namely Support Vector Machine (SVM), k-Nearest Neighbour (KNN), Random Forest (RF) and Decision Tree (DT) to classify the images and detect possibility of cancerous image. This study also discusses about the SIFT and SURF features extraction techniques and a critical analysis of performance of various machine learning techniques have been presented.en_US
dc.subjectBreast Canceren_US
dc.subjectClassification Techniquesen_US
dc.subjectFeatures Extractionen_US
dc.titleBreast Cancer detection from Thermograms Using Feature Extraction and Machine Learning Techniquesen_US
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