Breast cancer classification in ultras und oimages using transfer learning /
Ahmed Mostafa Salem Hijab
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
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