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Updated: Nov 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine.

Zhemin Zhuang¹, Zengbiao Yang¹, Shuxin Zhuang¹

¹Department of Electronic Engineering, Shantou University, Shantou 515063, China.

Computational Intelligence and Neuroscience

|June 11, 2021

Summary

This summary is machine-generated.

This study introduces a new breast tumor classification method using multi-features from ultrasound images and support vector machines. The approach achieved 92.5% accuracy in distinguishing cancerous from noncancerous tumors.

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Breast ultrasound is a standard diagnostic tool for breast tumors.
Accurate classification of breast tumors is crucial for effective treatment.

Purpose of the Study:

To develop and evaluate a novel multi-feature classification method for breast tumor diagnosis using ultrasound images.
To improve the accuracy of differentiating cancerous from noncancerous breast tumors.

Main Methods:

Lesion segmentation using an improved level set algorithm to extract characteristic features (orientation, edge indistinctness, posterior shadowing, shape).
Deep learning features extracted via transfer learning using a pretrained model.
Fusion of characteristic and deep learning features.
Classification using a support vector machine (SVM).

Main Results:

The proposed multi-feature model achieved a classification accuracy of 92.5% on unknown breast ultrasound samples.
Accurate segmentation enabled precise calculation of key image characteristics.
Deep learning features effectively complemented traditional characteristics.

Conclusions:

The combined characteristic and deep learning features approach with SVM demonstrates high efficacy for breast tumor classification.
This method offers a promising advancement for automated breast tumor diagnosis using ultrasound.
The technique has the potential to enhance diagnostic accuracy and efficiency in clinical settings.