Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Applying a random projection algorithm to optimize machine learning
  model for breast lesion classification   Machine learning is widely used in developing computer-aided diagnosis (CAD)
schemes of medical images. However, CAD usually computes large number of image
features from the targeted regions, which creates a challenge of how to
identify a small and optimal feature vector to build robust machine learning
models. In this study, we investigate feasibility of applying a random
projection algorithm to build an optimal feature vector from the initially
CAD-generated large feature pool and improve performance of machine learning
model. We assemble a retrospective dataset involving 1,487 cases of mammograms
in which 644 cases have confirmed malignant mass lesions and 843 have benign
lesions. A CAD scheme is first applied to segment mass regions and initially
compute 181 features. Then, support vector machine (SVM) models embedded with
several feature dimensionality reduction methods are built to predict
likelihood of lesions being malignant. All SVM models are trained and tested
using a leave-one-case-out cross-validation method. SVM generates a likelihood
score of each segmented mass region depicting on one-view mammogram. By fusion
of two scores of the same mass depicting on two-view mammograms, a case-based
likelihood score is also evaluated. Comparing with the principle component
analyses, nonnegative matrix factorization, and Chi-squared methods, SVM
embedded with the random projection algorithm yielded a significantly higher
case-based lesion classification performance with the area under ROC curve of
0.84+0.01 (p<0.02). The study demonstrates that the random project algorithm is
a promising method to generate optimal feature vectors to help improve
performance of machine learning models of medical images.

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The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg