Breast cancer classification in ultras und oimages using transfer learning /
تصنيف سرطان الثدى فى صور الموجات فوق الصوتية باستخدام تعلم النقل
Ahmed Mostafa Salem Hijab ; Supervised Ayman M. Eldeib , Muhammad A. Rushdi
- Cairo : Ahmed Mostafa Salem Hijab , 2020
- 54 P. : charts , facimiles ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
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
Breast lesion Convolutional neural networks Ultrasound