Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted  presents an approach for breast cancer diagnosis in digital mammograms using shearlet transform.A new approach is represented for feature extraction using shearlet transform, t-test statistic and dynamic thresholding.The system uses two datasets (ROIs) obtained from two different database; MIAS and DDSM database.The datasets include all abnormalites for breast cancer.The classification section was performed to distinguish the ROIs as normal-abnormal and benign-malignant. , I introduce a new method for feature extraction to classify digital mammograms using fast finite shearlet transform. Initially, fast finite shearlet transform was performed over mammogram images, and feature vectors re built using coefficients of the transform. In subsequent calculations, features re ranked according to t-test statistics, and capabilities re distinguished beten different classes. To maximize differences beten class representatives, a thresholding process was implemented as a final stage of feature extraction, and classifications re calculated over the optimal feature set using 5-fold cross validation and a support vector machine (SVM) classifier. The present results show that the proposed method provides satisfactory classification accuracy.
['Nebi Gedik']
I am writing a research paper on a idea A new feature extraction method based on multi-resolution representations of mammograms
[{"from": "human", "value": "I am writing a research paper on a idea A new feature extraction method based on multi-resolution representations of mammograms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted  presents an approach for breast cancer diagnosis in digital mammograms using shearlet transform.A new approach is represented for feature extraction using shearlet transform, t-test statistic and dynamic thresholding.The system uses two datasets (ROIs) obtained from two different database; MIAS and DDSM database.The datasets include all abnormalites for breast cancer.The classification section was performed to distinguish the ROIs as normal-abnormal and benign-malignant. , I introduce a new method for feature extraction to classify digital mammograms using fast finite shearlet transform. Initially, fast finite shearlet transform was performed over mammogram images, and feature vectors re built using coefficients of the transform. In subsequent calculations, features re ranked according to t-test statistics, and capabilities re distinguished beten different classes. To maximize differences beten class representatives, a thresholding process was implemented as a final stage of feature extraction, and classifications re calculated over the optimal feature set using 5-fold cross validation and a support vector machine (SVM) classifier. The present results show that the proposed method provides satisfactory classification accuracy."}]