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Record W4414079408 · doi:10.1109/jerm.2025.3600407

A Low-Frequency Breast Cancer Detection Setup: An Experimental Study

2025· article· en· W4414079408 on OpenAlexaff
Ghazaleh Tashtarian, Hamid Akbari–Chelaresi, Mauricio Hernandez, Abdolali Abdipour, Ahad Tavakoli, Omar M. Ramahi

Bibliographic record

VenueIEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResonatorBreast cancerMagnetic fieldSensitivity (control systems)Spiral (railway)Antenna (radio)Conductivity

Abstract

fetched live from OpenAlex

This paper presents a novel low-frequency electromagnetic system for enhanced breast tumor detection. The proposed setup utilizes a loop array integrated with spiral resonators as a magnetic field-driven transmitter, and a metasurface antenna as the receiver. Operating at 200 MHz, this system achieves deeper tissue penetration due to its low frequency and enhances sensitivity to anomalies because of the conductivity contrast between tumor and healthy tissues under magnetic fields. The uniform illumination of the entire breast volume eliminates the need for mechanical scanning. Furthermore, integrating the loop array with the spiral resonators enhances the magnetic field strength, while the single-feed-point design simplifies the system. Numerical simulations were conducted using a realistic dense breast model, demonstrating that the proposed setup can detect tumors of various locations, sizes, and depths within the dense breast tissue. The complete system was fabricated, implemented, and validated through experimental measurements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.337
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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