Computational Intelligence Re-meets Medical Image Processing
A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical
Image Registration
Summary
Background Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized
by inflammation of lung tissue, which may lead to permanent loss of the ability to
breathe and death. Distinguishing among these diseases is challenging to physicians
due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful
approach to improve diagnostic accuracy, by combining information provided by experts
with Machine Learning (ML) methods.
Objectives Exploring the potential of dimensionality reduction combined with ML methods for
diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods.
Methods A data set composed of 3252 regions of interest (ROIs) was used, from which 28 features
were extracted per ROI. We used Principal Component Analysis, Linear Discriminant
Analysis, and Stepwise Selection – Forward, Backward, and Forward-Backward to reduce
feature dimensionality. The feature subsets obtained were used as input to the following
ML methods: Support Vector Machine, Gaussian Mixture Model, k-Nearest Neighbor, and
Deep Feedforward Neural Network. We also applied a Deep Convolutional Neural Network
directly to the ROIs.
Results We achieved the maximum reduction from 28 to 5 dimensions using LDA. The best classification
results were obtained by DFNN, with 99.60% of overall accuracy.
Conclusions This work contributes to the analysis and selection of features that can efficiently
characterize the DLDs studied.
Keywords
Deep learning - diffuse lung diseases - dimensionality reduction - machine learning
