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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Inception U-Net for Enhanced Breast Ultrasound Image Segmentation Using Transfer Learning.

Yeonhyo Choi¹, Myoung Nam Kim², Sungdae Na³

¹Department of Medical & Biological Engineering, Graduate School, Kyungpook National University, Daegu 41404, Republic of Korea.

Bioengineering (Basel, Switzerland)

|February 27, 2026

Summary

This study enhances breast cancer segmentation in ultrasound images by integrating Inception modules into the U-Net architecture. The improved model shows a ~5% performance boost, advancing automated medical image analysis.

Keywords:

U-Net breast ultrasound deep learning image segmentation inception medical imaging transfer learning

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Image Segmentation

Background:

Breast cancer diagnosis relies heavily on ultrasound imaging.
Operator dependency and image quality issues hinder traditional methods.
Existing U-Net models have limitations in feature extraction due to shallow encoders.

Purpose of the Study:

To develop an enhanced segmentation model for breast ultrasound images.
To improve feature extraction capabilities beyond traditional U-Net architectures.
To leverage transfer learning for better segmentation performance.

Main Methods:

Replaced the U-Net encoder with an Inception architecture.
Utilized transfer learning with ImageNet pre-trained weights.
Trained and evaluated the model on 900 breast ultrasound images.

Main Results:

The Inception U-Net achieved an IoU of 0.7774 and Dice score of 0.8491.
Demonstrated approximately 5% improvement over the baseline U-Net.
Achieved precision of 0.7081 and recall of 0.7174.

Conclusions:

Inception modules enhance feature extraction for breast ultrasound segmentation.
Transfer learning from ImageNet is effective despite domain differences.
The approach provides a foundation for advanced medical imaging applications.