Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique

TL;DR

This paper introduces a local clustering method based on sparse solutions for lung cancer image classification, utilizing graph-based techniques and wavelet filtering to improve efficiency and accuracy over existing methods.

Contribution

The paper presents a novel local clustering approach using sparse linear system solutions combined with wavelet filtering for enhanced lung cancer image classification.

Findings

Effective classification performance demonstrated

Outperforms traditional clustering methods

Efficient computational approach

Abstract

In this work, we propose to use a local clustering approach based on the sparse solution technique to study the medical image, especially the lung cancer image classification task. We view images as the vertices in a weighted graph and the similarity between a pair of images as the edges in the graph. The vertices within the same cluster can be assumed to share similar features and properties, thus making the applications of graph clustering techniques very useful for image classification. Recently, the approach based on the sparse solutions of linear systems for graph clustering has been found to identify clusters more efficiently than traditional clustering methods such as spectral clustering. We propose to use the two newly developed local clustering methods based on sparse solution of linear system for image classification. In addition, we employ a box spline-based tight-wavelet-framelet method to clean these images and help build a better adjacency matrix before clustering. The performance of our methods is shown to be very effective in classifying images. Our approach is significantly more efficient and either favorable or equally effective compared with other state-of-the-art approaches. Finally, we shall make a remark by pointing out two image deformation methods to build up more artificial image data to increase the number of labeled images.