Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/3277
Title: | Breast Cancer detection from Thermograms Using Feature Extraction and Machine Learning Techniques |
Authors: | Mishra, Vartika Singh, Yamini Rath, Santanu Kumar |
Keywords: | Breast Cancer Thermography Classification Techniques Features Extraction |
Issue Date: | Mar-2019 |
Citation: | 5th International Conference for Convergence in Technology (I2CT 2019), Pune, India, 29-31 March 2019 |
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. |
Description: | Copyright of this document belongs to proceedings publisher. |
URI: | http://hdl.handle.net/2080/3277 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2019_I2CT_YSingh_BreastCancer.pdf | Conference paper | 155.69 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.