PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images
Table 3
MATLAB implementation and parameter settings of the PFP-LHCINCA model.
Method
Parameter
Image resizing
256 × 256
Image decomposition
Average pooling with four levels using 2 × 2, 4 × 4, 8 × 8, and 16 × 16
Patch division
16 × 16 sized patches
LPQ and HOG feature extraction
341 (256 LPQ and 36 HOG) features are extracted for each patch
Feature merging
The concatenation function is merged
Chi2
The most informative 1000 features are selected
INCA
Range: [100, 1000]; error function: kNN with 10-fold CV. Herein, k is 1, the distance metric is Euclidean, and weight is none
Classifiers
kNN: k = 70, distance: correlation, weight: squared inverse