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Using deep learning for mammography classification
Pinar Uskaner Hepsaǧ
, Selma Ayşe Özel
,
Adnan Yazici
Department of Electrical and Computer Engineering
Cukurova University
Middle East Technical University
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
64
Citations (SciVal)
Overview
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Keyphrases
Deep Learning
100%
Preprocessing Techniques
100%
Mammogram Classification
100%
Image Data
66%
Classification Accuracy
66%
Precision-recall
66%
Unnecessary Biopsy
66%
Diagnostic Methods
33%
Negative Impact
33%
Mini
33%
Convolutional Neural Network
33%
Ultrasound
33%
Classification Performance
33%
Region of Interest
33%
Mammogram Images
33%
Score Value
33%
Mammography
33%
Breast Cancer Diagnosis
33%
MIAS Database
33%
Benign or Malignant
33%
Crop Image
33%
Computer-aided Diagnostics
33%
Negative Biopsy
33%
Mini-MIAS Database
33%
Breast Biopsy
33%
INIS
classification
100%
datasets
100%
learning
100%
biopsy
100%
accuracy
75%
images
75%
data
50%
precision
50%
breasts
50%
cancer
25%
cost
25%
performance
25%
information
25%
values
25%
operation
25%
computers
25%
pain
25%
neural networks
25%
diagnostic techniques
25%
augmentation
25%
crops
25%
masks
25%
Computer Science
Deep Learning
100%
Classification Accuracy
100%
Convolutional Neural Network
50%
Classification Performance
50%
Negative Impact
50%