2018
DOI: 10.1109/jbhi.2017.2740500
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Designing of Ground-Truth-Annotated DBT-TU-JU Breast Thermogram Database Toward Early Abnormality Prediction

Abstract: The advancement of research in a specific area of clinical diagnosis crucially depends on the availability and quality of the radiology and other test related databases accompanied by ground truth and additional necessary medical findings. This paper describes the creation of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) breast thermogram database. The objective of creating the DBT-TU-JU database is to provide a breast thermogram database that is annotated with the ground-t… Show more

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Cited by 40 publications
(11 citation statements)
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“…RF [20,27]: RF classifier is designed based on combining multiple decision trees to obtain enhanced accuracy of classification. The required parameters for RF with values used for this study are number of tree: 100, quality of each split: Gini, the maximum number of feature for best split: auto, maximum tree depth: None, the minimum number of sample for the split: 20, the minimum size of end node/leaf:1, minimum weight fraction leaf: 0.0, maximum leaf node size: None, minimum impurity decrease: 0.0, bootstrap: True, cross-validation method (oob_score): False, processor number: None, random state: None, verbosity:0, warm_start: False, balanced subsample weight: None.…”
Section: Classificationmentioning
confidence: 99%
“…RF [20,27]: RF classifier is designed based on combining multiple decision trees to obtain enhanced accuracy of classification. The required parameters for RF with values used for this study are number of tree: 100, quality of each split: Gini, the maximum number of feature for best split: auto, maximum tree depth: None, the minimum number of sample for the split: 20, the minimum size of end node/leaf:1, minimum weight fraction leaf: 0.0, maximum leaf node size: None, minimum impurity decrease: 0.0, bootstrap: True, cross-validation method (oob_score): False, processor number: None, random state: None, verbosity:0, warm_start: False, balanced subsample weight: None.…”
Section: Classificationmentioning
confidence: 99%
“…In the same year, Bhowmik et al [58] also proposed a standard breast thermogram acquisition protocol. Factors influencing thermography such as patient's personal and medical information as well as room condition were adjusted before the examination.…”
Section: B Breast Thermographymentioning
confidence: 99%
“…This method obtained an accuracy of 90.90% using a decision tree classifier. In [35], a ground-truth-annotated breast thermogram database (DBT-TU-JU database) was proposed for the early prediction of breast lesions. The DBT-TU-JU database includes 1100 static thermograms of 100 cases.…”
Section: Related Workmentioning
confidence: 99%