An Efficient Hybrid Classifier for Cancer Detection

Yangyang Chang, Fadi Abu-Amara


The early detection of cancer in both healthy and high-risk populations offers increased opportunity for treatment and curative intent. In this paper, we propose a hybrid classifier that produces an efficient classification system for cancer detection in cell datasets. The first part of this work investigates the performance of artificial neural networks (ANN) such as Self-Organizing Feature Map (SOM) and Learning Vector Quantization (LVQ), while in the second part, we present our investigation on the performances of Decision Tree (DT) and its pruning model. We also, in the third part, present our proposal for a new hybrid classifier that is based on the Random Forest (RF) and the combination of the LVQ and DT. Experimental results of the proposed hybrid classifier indicate that the hybrid classifier effectively avoids the drawbacks of individual classifiers and has high anti-noise performance.


Self-Organizing Map, Learning Vector Quantization, Decision Tree, Cancer Detection, Hybrid Classifier, Bootstrap Sampling

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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