Evidence map›Paper›PMID 41362664›Full record

ReviewMethodsX2025

Microwave head imaging systems for early brain tumor detection: antenna designs and emerging substrates.

Jinu Mathew, Osamah Ibrahim Khalaf, Navin M George, Ancy Michel, Neethan Elizabeth Abraham, Deema Mohammed Alsekait, Sharf Alzu'bi, Diaa Salama AbdElminaam

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jinu MathewDepartment of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.
Osamah Ibrahim KhalafAl-Nahrain University, Al-Nahrain Renewable Energy Research Center, Baghdad, Iraq.
Navin M GeorgeDepartment of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.
Ancy MichelDepartment of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.
Neethan Elizabeth AbrahamDepartment of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.
Deema Mohammed AlsekaitDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sharf Alzu'biDepartment of Information Technology, College of Engineering and Technology, Royal University for Women, West Riffa, Bahrain.
Diaa Salama AbdElminaamFaculty of computers and Artificial Intelligence, Benha University, Benha, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection of brain tumors is essential for successful treatment and better patient outcomes. Traditional imaging methods like X-rays, MRIs, CT scans, and PET scans have been important in detecting brain tumors, but they are expensive with many drawbacks and areas where access is limited. Antenna-based method has recently emerged as a practical alternative for real-time, non-invasive detection of brain tumors. This paper explores different antennas and various types of substrates that are adaptable to human sensitive tissues for detecting brain tumors. This review highlights the antenna working principles, and the advantages and challenges associated with each type. The effectiveness of several antenna-based methods in medical diagnostics, including microwave imaging and ultra-wideband (UWB) systems, is discussed. To assess their impact on detection accuracy, essential factors such as penetration depth, resolution, operating frequency, and antenna design are considered. The integration of antennas with machine learning and signal processing techniques is investigated.

Indexed as

Artificial intelligence and machine learningBrain tumorGainMicrowave head imaging antennaReflection co-efficientSpecific absorption rate (SAR)Substrate

Identifiers

PMID41362664
PMCPMC12681771

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.