IMPROVED ORAL CANCER CLASSIFICATION USING HYBRIDIZATION OF DEEP FEATURE AND CORRELATION-BASED FEATURE WEIGHT
DOI:
https://doi.org/10.48047/hqkefd72Keywords:
Deep Learning, Feature Classification, Binary Cancer Detection, Medical Images, ResNet18, EfficientNet-B0, Feature Relevance, Medical Image AnalysisAbstract
This study presents a hybrid deep learning-based feature classification framework for binary cancer detection using medical images. The proposed method extracts heterogeneous deep features from ResNet18 and EfficientNet-B0, which are then concatenated to form a unified feature vector. To quantify feature relevance, the Pearson correlation between each feature dimension and the ground-truth labels is computed. These correlation scores are then used to weight each feature dimension, producing a correlation-enhanced representation. The weighted feature matrix is then used to train multiple classifiers, including Random Forest, SVM, XGBoost, Extra Trees, and Logistic Regression.
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