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Related Experiment Video

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Classification of Cancer Tissue With Machine Learning Algorithms Using Microwave Datasets.

Rabia Toprak¹, Huseyin Duysak¹, Zeliha Esin Celik²

¹Department of Electrical-Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey.

Bioelectromagnetics

|January 6, 2026

Summary

Microwave measurements can rapidly distinguish cancerous from healthy colon tissue. The k-nearest neighbors algorithm achieved the highest accuracy, speeding up cancer diagnosis.

Keywords:

free‐space measurement horn antenna machine learning microwave tumorous colon tissue classification

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Area of Science:

Bioengineering
Medical Physics
Computational Biology

Background:

Rising cancer incidence necessitates faster diagnostic methods.
Traditional pathology reports are time-consuming.
Early cancer detection is critical for treatment success.

Purpose of the Study:

To develop a rapid method for differentiating cancerous from healthy colon tissue.
To evaluate microwave measurement techniques for pathological tissue analysis.
To compare classification algorithms for accuracy in tissue identification.

Main Methods:

Utilized free-space microwave measurements (18-26 GHz) on colon tissue samples.
Collected scattering parameters, including reflection and transmission coefficients.
Trained and tested k-nearest neighbors (KNN), artificial neural networks (ANN), and Support Vector Machines (SVM) algorithms.

Main Results:

Four distinct datasets were created using various combinations of measurement features.
The KNN algorithm demonstrated the highest classification accuracy.
Optimal performance was achieved using reflection, transmission coefficients, and frequency data.

Conclusions:

Microwave measurements offer a promising, expedited approach for cancer diagnosis.
The KNN algorithm is effective for classifying colon tissue based on microwave data.
This technique has the potential to significantly reduce diagnostic turnaround times.