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Conference 12033 > Paper 12033-119
Paper 12033-119

Fusion of handcrafted and deep transfer learning features to improve performance of breast lesion classification using a relief based algorithm for optimal feature selection

In person: 23 February 2022 • 5:30 PM - 7:00 PM PST

Abstract

This study aims to demonstrate the feasibility of using the fusion of optimally selected handcrafted and automated features to build a machine learning classifier that can improve performance of classifying breast lesions. A retrospective dataset involving 1,535 mammograms depicting 740 malignant and 795 benign lesions is used. 41 handcrafted features and 25,088 automated features extracted from a pre-trained VGG16 are initially computed. After applying relief-based algorithms to select optimal features, three linear SVMs are trained using a 10-fold cross-validation method. Three SVM trained using handcrafted, automated, and fusion of two type features yield AUCs of 0.621, 0.668 and 0.710, respectively.

Presenter

The Univ. of Oklahoma (United States)
Presenter/Author
The Univ. of Oklahoma (United States)
Author
The Univ. of Oklahoma (United States)
Author
The Univ. of Oklahoma (United States)
Author
The Univ. of Oklahoma (United States)