TY  - BOOK
AU  - Ahmed Mostafa Salem Hijab
AU  - Ayman M. Eldeib , 
AU  - Muhammad A. Rushdi , 
TI  - Breast cancer classification in ultras und oimages using transfer learning / 
PY  - 2020///
CY  - Cairo : 
PB  - Ahmed Mostafa Salem Hijab , 
KW  - Breast lesion
KW  - Convolutional neural networks
KW  - Ultrasound
N1  - Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering; Issued also as CD
N2  - We explored three versions of a deep learning solution to computer-aided detection of ultrasound images of cancerous tumor tissues. Experimentally, our work proved that the pre-trained VGG16 model has the best outputs in the fine-tuned version. In short, our test accuracy ranges from 79% to 97%. We employed data augmentation to enlarge the amount of training data, and avoid overfitting. We have also employed the VGG16 pre-trained model, and added practical fine tuning to improve precision.This work offers a path into developing realistic and versatile deep learning frameworks for detecting breast cancer.The findings suggest that the fine-tuned model with pre-training medical data has increased the classification accuracy.These frameworks should complement and provide assistance for approaches of clinical diagnosis and treatment
ER  -