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

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.