Enhanced Histopathological Image Classification through the fusion of Thepade Sorted Block Truncation Code and Otsu Binarization features

Year 2024, Volume: 11 Issue: 2, 175 - 185, 07.07.2024

Ashwin Acharya , Sudeep Thepade

https://doi.org/10.31202/ecjse.1380112

Abstract

Histopathology is the branch of pathology that investigates the structure of cells and tissues of organisms at a microscopic level. Histopathological images are crucial in the decision-making process for effective therapies, determining the health of a particular biological structure and identifying diseases like cancer. With machine learning models, it may be feasible to increase the accuracy of medical data, decrease patient rate variations, and cut costs associated with medical care. Most medical scientists are drawn to such new technologies of predictive models in chronic disease forecasting. A novel approach for more accurate classification of histopathological images is proposed in this paper. The technique involves fusing the features extracted from two methods, namely Otsu's binarization and Thepade Sorted Block Truncation Code, to achieve improved results. The KIMIA Path960 dataset comprising 960 images is utilized for experimental validation with performance indicators like accuracy, specificity, and sensitivity. Ensembles of Simple Logistics, Multilayer Perceptron, Logistics Model Tree, as well as Simple Logistics, Random Forest, and Logistic Model Tree classifiers, demonstrated superior performance for the fusion of Thepade Sorted Block Truncation Code 7-ary and Otsu features, achieving an accuracy of 97.39 percent in a 10-fold cross-validation scenario.

Keywords

Histopathology, Thepade SBTC, Feature Fusion, Ensembles

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