Ensemble Machine Learning based Enhanced Detection of Lymph Node Cancer Through Histopathology Images with Synergistic Fusion of Thepade SBTC and Color Binning Features
Keywords:
Thepade Sorted Block Truncation Coding (Thepade SBTC), Color Binning, Histopathology Cancer Detection, Machine Learning, Ensemble Learning, Logistic regression, AdaBoost classifier, K-nearest neighbors (KNN), XGBoost, Decision trees, CatBoost, Gaussian Naive Bayes classifier, Random forests, Neural networksAbstract
Histopathology cancer detection plays a crucial part in primordial diagnosis and treatment planning. This research introduces a novel approach combining the features formed with color binning and Thepade Sorted Block Truncation Coding (Thepade SBTC) methods for cancer detection from histopathology images using machine learning (ML) algorithms. The proposed feature fusion technique leverages the strengths of Thepade SBTC, enhancing its performance through the integration of color binning. The study evaluates the efficacy of these features across a range of dimensions, from 2-bin to 15-bin in color binning and 2-ary to 15-ary in Thepade SBTC, shedding light on the impact of feature granularity on classification accuracy.
The research compares the accuracy of suggested feature fusion-based cancer detection with the features using Thepade SBTC and color binning individually, for various ML algorithms such as Logistic Regression, XGBoost, K-Nearest Neighbors(KNN), Decision Tree(DT), CatBoost, Gaussian Naive Bayes(NB), Random Forest(RF), AdaBoost, and Neural Network. Results indicate that KNN, Random Forest, and CatBoost consistently outperform other algorithms in both Thepade SBTC and color binning scenarios.
Motivated by these findings, a novel ensemble approach is proposed, utilizing a Voting Classifier with KNN, Random Forest, and CatBoost as base estimators. The ensemble model achieves an impressive 90% accuracy by cascading features extracted from 6-bin color binning and 14-ary Thepade SBTC. This highlights the potential for synergistic combinations of feature extraction methods and ensemble learning in Histopathology cancer detection.
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