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Breast cancer classification in ultras und oimages using transfer learning / Ahmed Mostafa Salem Hijab ; Supervised Ayman M. Eldeib , Muhammad A. Rushdi

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ahmed Mostafa Salem Hijab , 2020Description: 54 P. : charts , facimiles ; 30cmOther title:
  • تصنيف سرطان الثدى فى صور الموجات فوق الصوتية باستخدام تعلم النقل [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: 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
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2020.Ah.B (Browse shelf(Opens below)) Not for loan 01010110082827000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2020.Ah.B (Browse shelf(Opens below)) 82827.CD Not for loan 01020110082827000

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

Issued also as CD

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