2 research outputs found
Automatic multi-objective based feature selection for classification
Objective: Accurately classifying the malignancy of lesions detected in a
screening scan is critical for reducing false positives. Radiomics holds great
potential to differentiate malignant from benign tumors by extracting and
analyzing a large number of quantitative image features. Since not all radiomic
features contribute to an effective classifying model, selecting an optimal
feature subset is critical. Methods: This work proposes a new multi-objective
based feature selection (MO-FS) algorithm that considers sensitivity and
specificity simultaneously as the objective functions during feature selection.
For MO-FS, we developed a modified entropy based termination criterion (METC)
that stops the algorithm automatically rather than relying on a preset number
of generations. We also designed a solution selection methodology for
multi-objective learning that uses the evidential reasoning approach (SMOLER)
to automatically select the optimal solution from the Pareto-optimal set.
Furthermore, we developed an adaptive mutation operation to generate the
mutation probability in MO-FS automatically. Results: We evaluated the MO-FS
for classifying lung nodule malignancy in low-dose CT and breast lesion
malignancy in digital breast tomosynthesis. Conclusion: The experimental
results demonstrated that the feature set selected by MO-FS achieved better
classification performance than features selected by other commonly used
methods. Significance: The proposed method is general and more effective
radiomic feature selection strategy
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
To predict lung nodule malignancy with a high sensitivity and specificity, we
propose a fusion algorithm that combines handcrafted features (HF) into the
features learned at the output layer of a 3D deep convolutional neural network
(CNN). First, we extracted twenty-nine handcrafted features, including nine
intensity features, eight geometric features, and twelve texture features based
on grey-level co-occurrence matrix (GLCM) averaged from thirteen directions. We
then trained 3D CNNs modified from three state-of-the-art 2D CNN architectures
(AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at
the output layer. For each 3D CNN, the CNN features combined with the 29
handcrafted features were used as the input for the support vector machine
(SVM) coupled with the sequential forward feature selection (SFS) method to
select the optimal feature subset and construct the classifiers. The fusion
algorithm takes full advantage of the handcrafted features and the highest
level CNN features learned at the output layer. It can overcome the
disadvantage of the handcrafted features that may not fully reflect the unique
characteristics of a particular lesion by combining the intrinsic CNN features.
Meanwhile, it also alleviates the requirement of a large scale annotated
dataset for the CNNs based on the complementary of handcrafted features. The
patient cohort includes 431 malignant nodules and 795 benign nodules extracted
from the LIDC/IDRI database. For each investigated CNN architecture, the
proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and
specificity scores among all competitive classification models.Comment: 11 pages, 5 figures, 5 tables. This work has been submitted to the
IEEE for possible publicatio