Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data

TL;DR

This paper introduces a saliency-based folding method for medical image classification that focuses on relevant regions, reducing computational costs while maintaining high accuracy in SVM-based classification using LBP features.

Contribution

It proposes a novel saliency detection and folding technique to improve medical image classification efficiency and accuracy with reduced data processing.

Findings

Achieves comparable accuracy to state-of-the-art methods

Reduces computational cost and storage requirements

Effective on large-scale IRMA 2009 x-ray dataset

Abstract

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.