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

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Multi-Instance Classification of Breast Tumor Ultrasound Images Using Convolutional Neural Networks and Transfer

Alexandru Ciobotaru¹, Maria Aurora Bota², Dan Ioan Goța¹

¹Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

Bioengineering (Basel, Switzerland)

|December 23, 2023

Summary

This study developed a custom deep learning model for breast ultrasound image classification. The custom model achieved high accuracy, outperforming several state-of-the-art models in detecting benign and malignant breast masses.

Keywords:

Convolutional Neural Networks breast cancer computer vision deep learning transfer learning ultrasound images

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer is a leading cause of death in women globally.
Automated early detection and classification of breast masses are crucial for improving patient outcomes.
Ultrasound imaging is vital for breast cancer diagnosis, but its accuracy depends on specialist expertise.

Purpose of the Study:

To compare the efficiency of six deep learning models for classifying breast ultrasound images.
To introduce and evaluate a custom deep learning model for breast mass classification.
To address the need for fast and reliable automated detection algorithms in breast cancer diagnostics.

Main Methods:

Transfer learning was used to fine-tune six state-of-the-art deep learning models (ResNet-50, Inception-V3, Inception-ResNet-V2, MobileNet-V2, VGG-16, DenseNet-121).
A custom deep learning model was developed and trained from scratch on augmented ultrasound image datasets.
Model performance was evaluated using Precision, Recall, F1-Score, and Specificity on public and private datasets.

Main Results:

Models trained on an augmented dataset of 7800 images demonstrated superior performance.
The custom model achieved high accuracy (96.75 ± 0.26%) on a private dataset, outperforming some established models.
Specific state-of-the-art models like DenseNet-121 and VGG-16 showed high accuracy (98.11 ± 0.10% and 97.77 ± 0.29%, respectively).

Conclusions:

The custom-developed deep learning model demonstrates competitive performance in classifying breast ultrasound images.
The proposed model achieved high accuracy and efficient training times, suggesting its potential for clinical application.
This research contributes to developing reliable automated systems for breast cancer early detection.