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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in

Alessandro Stefano¹, Fabiano Bini², Eleonora Giovagnoli²

¹Institute of Bioimaging and Complex Biological Systems, National Research Council (IBSBC-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy.

Diagnostics (Basel, Switzerland)

|May 1, 2025

Summary

Deep learning significantly outperforms machine learning radiomics for breast cancer diagnosis from mammograms. This advancement offers improved accuracy for classifying lesions, aiding clinicians in better patient management.

Keywords:

automated diagnostic systems breast lesion classification deep learning mammography radiomics

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Breast cancer is a leading cause of mortality in women, with early diagnosis via mammography crucial for survival.
Manual interpretation of mammograms is challenging, necessitating automated tools for improved accuracy and efficiency.
This study evaluates machine learning (ML) radiomics against deep learning (DL) for breast lesion classification.

Purpose of the Study:

To compare the diagnostic performance of ML-based radiomics and DL models for classifying breast lesions.
To assess the effectiveness of these automated approaches in distinguishing benign from malignant findings on mammograms.
To determine which AI methodology offers superior accuracy for breast cancer diagnosis.

Main Methods:

Radiomics features were extracted using matRadiomics from 1219 mammograms (CBIS-DDSM database).
Linear Discriminant Analysis (LDA) was the best-performing ML model, validated externally on 222 images.
The EfficientNetB6 deep learning model was implemented for comparative analysis.

Main Results:

LDA achieved moderate AUCs (68.28% microcalcifications, 61.53% masses) in validation.
External validation showed similar LDA performance (66.9% microcalcifications, 61.5% masses).
EfficientNetB6 DL model significantly outperformed LDA, reaching AUCs of 81.52% (microcalcifications) and 76.24% (masses).

Conclusions:

Machine learning radiomics shows limitations in accurately diagnosing breast lesions from mammograms.
Deep learning, specifically EfficientNetB6, demonstrates superior performance and enhanced diagnostic accuracy.
DL holds significant potential to aid clinicians in improving breast cancer diagnosis and patient management.