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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Related Experiment Video

Updated: Jun 12, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images.

P Vijaya¹, Satish Chander², Roshan Fernandes³

¹Department of Mathematics & Computer Science, Modern College of Business and Sciences, Muscat, Oman.

Cancer Investigation

|September 20, 2024

Summary

This study introduces an advanced method for early breast cancer detection using Breast Magnetic Resonance Imaging (MRI). The novel Flamingo Search SailFish Optimizer (FSSFO) enhances deep learning models for more accurate identification of breast cancer.

Keywords:

Flamingo Search Algorithm (FSA)SailFish Optimizer (SFO)SqueezeNet median filter segmentation

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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

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Last Updated: Jun 12, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

Published on: August 14, 2012

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 in Oncology
Computational Biology

Background:

Breast cancer is a leading cancer affecting women, necessitating accurate and early detection methods.
Breast Magnetic Resonance Imaging (MRI) is crucial for diagnosis and treatment evaluation but can be time-consuming.
Early detection significantly improves patient outcomes and treatment efficacy.

Purpose of the Study:

To develop an efficient and accurate system for early breast cancer detection using Breast MRI.
To enhance the performance of deep learning models in breast cancer identification.
To introduce a novel optimization algorithm for training these models.

Main Methods:

A multi-step process involving pre-processing (median filter), segmentation (Psi-Net), and augmentation (shearing, translation, cropping).
Feature extraction including shape features, Completed Local Binary Pattern (CLBP), Pyramid Histogram of Oriented Gradients (PHOG), and statistical features.
Utilized a deep learning model (SqueezeNet) for cancer detection, trained with the new Flamingo Search SailFish Optimizer (FSSFO), a hybrid of Flamingo Search Algorithm (FSA) and SailFish Optimizer (SFO).

Main Results:

The proposed methodology successfully integrates image processing techniques with deep learning for breast cancer detection.
The novel FSSFO algorithm demonstrates effectiveness in optimizing the training of Psi-Net and SqueezeNet models.
The combined approach aims to improve the accuracy and efficiency of breast cancer diagnosis from MRI scans.

Conclusions:

The developed system offers a promising approach for enhancing early breast cancer detection through automated analysis of Breast MRI.
The integration of FSSFO with deep learning models presents a significant advancement in computational methods for oncology.
Further validation is recommended to establish the clinical utility of this advanced breast cancer detection system.