The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an “end-to-end” training app...
Initial Requirements A Windows based machine WinSCP Cygwin (32 bit) MATLAB 201x Tested on Dell Inspiron 15 3537 Intel i5 4200U 4GB RAM Windows 10 with MATLAB R2015b Contact Anmol Sharmaanmol.sharma293@gmail.com Detailed Instructions along with images are present in Tutorial.pdf file. ...
To study the occurrence of breast cancer after the initial screen, we excluded 34 women with a prior diagnosis of breast cancer and 62 women whose breast cancer was detected within 6 months of recruitment (i.e., breast cancer potentially detected at the first screen). Women aged above 64 ...
The section of the European guidelines for mammography screening which defines the ideal requirements for breast units introduces a new concept of the interrelationship between population-based screening activities and clinical breast care services [75]. It is stated that organised screening programmes ...
Current deep learning algorithms need large amounts of training data to overcome the problems of overfitting. Existing breast cancer detection methods are computationally complex and require more treatment time to identify accurate tumors. In addition, manual breast cancer diagnosis can take months and ...
In general, as the increase of training database size and diversity, the performance and the robustness of the CAD schemes improve when they are tested using the independent testing datasets [75]. CAD schemes using CBIR approaches share two common characteristics. First, they use the "lazy" ...