Classification of histopathological breast cancer images using iterative VMD aided Zernike moments & textural signatures

TL;DR

This paper introduces a novel automated method for classifying breast cancer histopathological images using iterative VMD, Zernike moments, and textural features, achieving high accuracy on a large public dataset.

Contribution

The study proposes a new combination of multilevel iterative VMD and advanced feature extraction for improved breast cancer image classification.

Findings

Achieved average classification accuracy of 89.61% with three-fold cross-validation.

Outperformed existing state-of-the-art methods on the BreaKHis dataset.

Demonstrated the effectiveness of combining VMD with Zernike moments and entropy features.

Abstract

In this paper we present a novel method for an automated diagnosis of breast carcinoma through multilevel iterative variational mode decomposition (VMD) and textural features encompassing Zernaike moments, fractal dimension and entropy features namely, Kapoor entropy, Renyi entropy, Yager entropy features are extracted from VMD components. The proposed method considers the histopathological image as a set of multidimensional spatially-evolving signals. ReliefF algorithm is used to select the discriminatory features and statistically most significant features are fed to squares support vector machine (SVM) for classification. We evaluate the efficiency of the proposed methodology on publicly available Breakhis dataset containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results shows the efficacy of the proposed method in outperforming the state of the art while achieving an average classification rates of 89.61% and 88:23% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the pathologist in accurate and reliable diagnosis of biopsy samples. BreaKHis, a publicly dataset available at http://web.inf.ufpr.br/vri/breast-cancer-database.