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Updated: Jun 4, 2025

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

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

Published on: November 30, 2022

Segmentation for mammography classification utilizing deep convolutional neural network.

Dip Kumar Saha¹, Tuhin Hossain², Mejdl Safran³

¹Department of Computer Science and Engineering, Stamford University Bangladesh, Siddeswari, Dhaka, Bangladesh.

BMC Medical Imaging

|December 19, 2024

Summary

This summary is machine-generated.

A modified transformer model accurately distinguishes benign and malignant breast tissues in mammograms, achieving 99.96% accuracy. This deep learning approach aids early breast cancer (BC) diagnosis and treatment planning.

Keywords:

Breast cancer Classification Mammography SAM Segmentation

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Last Updated: Jun 4, 2025

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

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

Published on: November 30, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Mammography is crucial for early breast cancer (BC) detection, but differentiating benign from malignant masses can be challenging.
Deep learning (DL) computer-aided diagnosis (CAD) models are increasingly used for BC classification.

Purpose of the Study:

To evaluate an improved transformer model for distinguishing benign from malignant breast tissues in mammograms.
To enhance region of interest (ROI) extraction using the segmentation anything model (SAM).

Main Methods:

Utilized the INbreast dataset containing benign and malignant breast tissues.
Modified a pyramid transformer (PTr) architecture for BC identification.
Employed transfer learning (TL) and SAM for optimized ROI extraction.

Main Results:

The proposed PTr model achieved 99.96% accuracy and 99.98% AUC for binary classification.
Performance was compared against Vision Transformers (ViT), MobileNetV3, and EfficientNetB7.

Conclusions:

A modified transformer model with segmentation effectively classifies breast tissues from mammography images.
Accurate classification of benign and malignant tissues supports radiologists in treatment decisions.