A Multi-Criterion Selection of Hybrid Features in Mammographic Imaging for Early Computer-Assisted Sensing and Detection of Breast Cancer.

Amira J Zaylaa, Lama N Yassine, Silva Kourtian

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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

3 authors.

Amira J ZaylaaDepartment of Electrical and Computer Engineering, Faculty of Engineering, Beirut Arab University, Debbieh P.O. Box 11-5020, Lebanon.ORCID 0000-0001-8420-5668

Lama N YassineDepartment of Electrical and Computer Engineering, Faculty of Engineering, Beirut Arab University, Debbieh P.O. Box 11-5020, Lebanon.ORCID 0009-0001-2312-5098

Silva KourtianCentre de Recherche du Centre Hospitalier, L'Université de Montréal, Montréal, QC H2X 0A9, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Feature selection represents a critical step in developing accurate and interpretable models for early breast cancer detection. Despite extensive research in the field of mammographic image analysis, no consensus has yet been reached on the optimal feature subsets that distinguish normal from malignant tissues. To address this gap, the present study aims to identify the most discriminative and significant features through a comprehensive multi-criterion selection framework. The aim is to integrate, as new frameworks, different combinations of

Indexed as

Breast NeoplasmsEarly Detection of CancerMammographyAlgorithmsFemaleHumansRadiographic Image Interpretation, Computer-AssistedRadiomicsbreast cancerdeep learningearly detectionfeature selectionhealthcaremammographic imagesnew multi-criterion frameworkoptimal featuressensingstatistical evaluation

Identifiers

PMID42356847

DOI10.3390/s26123874

PMCPMC13306496

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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 OpenQuestion graph.