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Title: Classification of Mammography Images by Transfer Learning
Authors: Solak, Ahmet
Ceylan, Rahime
Keywords: Convolutional Neural Network
Transfer Learning
Feature Extraction
Fine Tuning
Data Augmentation
Issue Date: 2020
Publisher: IEEE
Abstract: Breast cancer is the most common cancer type in women worldwide. Diagnosis and early detection of cancer by mammography images are of great importance in cancer treatment. The use of deep learning in Computer Assisted Diagnostic systems has gained a great momentum especially since 2012. In this study, benign and malignant mass images were reproduced with data augmentation and the data sets obtained were classified with deep learning networks. In this study, a scratch Convolutional Neural Network (CNN) architecture was created and transfer learning was realized with different network models which trained on IMAGENET images. In the transfer learning section, separate training results were obtained by performing feature extraction and fine tuning of network parameters. As a result of the study, the best results were obtained with MobileNet, NASNetLarge and InceptionResNetV2 models which are used in transfer learning models.
Description: 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK
ISBN: 978-1-7281-7206-4
ISSN: 2165-0608
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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