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http://hdl.handle.net/2080/3277
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DC Field | Value | Language |
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dc.contributor.author | Mishra, Vartika | - |
dc.contributor.author | Singh, Yamini | - |
dc.contributor.author | Rath, Santanu Kumar | - |
dc.date.accessioned | 2019-04-04T11:28:54Z | - |
dc.date.available | 2019-04-04T11:28:54Z | - |
dc.date.issued | 2019-03 | - |
dc.identifier.citation | 5th International Conference for Convergence in Technology (I2CT 2019), Pune, India, 29-31 March 2019 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/3277 | - |
dc.description | Copyright of this document belongs to proceedings publisher. | en_US |
dc.description.abstract | Presence 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.subject | Breast Cancer | en_US |
dc.subject | Thermography | en_US |
dc.subject | Classification Techniques | en_US |
dc.subject | Features Extraction | en_US |
dc.title | Breast Cancer detection from Thermograms Using Feature Extraction and Machine Learning Techniques | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2019_I2CT_YSingh_BreastCancer.pdf | Conference paper | 155.69 kB | Adobe PDF | View/Open |
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