Title: Improved classification of histopathological images with feature fusion of Thepade SBTC and Sauvola thresholding using machine learning

Authors: Sudeep D. Thepade; Mangesh S. Dudhgaonkar Patil

Addresses: Computer Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India ' Computer Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India

Abstract: Histopathological images play a significant role in selecting effective therapeutics and identifying disorders like cancer. Digital histopathology is a crucial advancement in contemporary medicine. The growth and spread of cancer cells within the body can be significantly controlled or stopped with early identification and therapy. Many machine learning (ML) algorithms are used to study the images in the dataset. Feature extraction is done using Sauvola thresholding and Thepade sorted block truncation code (TSBTC). This paper presents a fusion of the features computed using the TSBTC and Sauvola thresholding method for improved classification of histopathological images. The experimental validation is done using 960 images from KIMIA PATH 960 dataset with the help of performance metrics like sensitivity, specificity, and accuracy. The superior performance is shown in TSBTC 9-ary and Sauvola thresholding feature fusion using logistic model tree (LMT) classifier with 97.6% accuracy in ten cross-fold validation scenarios.

Keywords: classification; binarisation; histopathological; feature fusion; ensembles; KIMIA_PATH_960; Thepade SBTC; classifiers.

DOI: 10.1504/IJCVR.2025.142925

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.1, pp.118 - 136

Received: 08 Feb 2023
Accepted: 30 Mar 2023
Published online: 02 Dec 2024 *

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