Therespective feature values were given as the input for a multi-objective classifier: Deep NeuralNetwork (DNN) for classifying the normal and abnormal regions in mammogram images. Theexperimental outcome shows that the proposed methodology improved accuracy in breastcancer classification up to 3% to ...
Young women, especially those who have not had children, usually have dense and rather firm breast tissue. Mammogram images like these can be difficult for a radiologist to read because there is less differentiation between normal and possibly abnormal tissue, potentially hiding areas that need close...
National Cancer Institute.Breast Health: Follow-up after an abnormal mammogram. Office on Women's Health.Polycystic ovary syndrome. American College of Obstetricians and Gynecologists.Benign nipple conditions. Suthar N, Pareek V, Nebhinani N, Suman D.Galactorrhea with antidepressants: A case series....
She had no previous personal history of breast biopsy or abnormal mammograms. Figure 1 Pedigree of the patient (indicated by arrow). She, one maternal aunt and one maternal cousin had breast cancer diagnosed at 36, 44 and 41 years old, respectively, as indicated by the half-filled symbols,...
Even in mammogram, considered as a golden standard technique for breast imaging, it is hard to pick up early occurring changes in the breast tissue due to the difference in mechanical behavior of the normal and abnormal tissue when subjected to compression prior to x-ray or laser exposure. In...
Because it has more tissue and less fat, the digitized mammography images are used for analyzing the abnormal areas of density, mass, and calcification that indicate the presence of cancer. In cases where adequate labeled data is available, deep learning approaches show considerable progress, and ...
Fatehia, B.G., Mawia, A.H.: Classification of breast tissue as normal or abnormal based on texture analysis of digital mammogram. Journal of Medical Imaging and Health Informatics (2014)F.B. Garma and M.A. Hassan, Classification of breast tissue as normal or abnormal based on texture ...