Evidence map›Paper›PMID 41522723›Full record

ArticleBioMed research international2026

XMP-Net: An XAI-Based Modified Xception Model for Recognizing Monkeypox and Other Skin Diseases.

Ferdib-Al-Islam, Prithvi Biswas, Partha Protim Gharami, Md Rahatul Islam

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Article in BioMed research international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

4 authors.

Ferdib-Al-IslamDepartment of Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh.ORCID 0000-0002-0758-2790
Prithvi BiswasDepartment of Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh.ORCID 0009-0003-8163-3474
Partha Protim GharamiDepartment of Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh.ORCID 0000-0002-6599-9148
Md Rahatul IslamGraduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan, kyutech.ac.jp.ORCID 0000-0002-5745-4439

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research introduces "XMP-Net," a modified Xception-based deep learning architecture constructed for the categorization of skin conditions, with a particular focus on identifying monkeypox. The study recognizes skin images of four categories: normal, chickenpox, measles, and monkeypox. To enhance interpretability and foster confidence in the model's predictions, Grad-CAM (gradient-weighted class activation mapping) and LIME (local interpretable model-agnostic explanations) were employed to illustrate the model's thinking manner. The model demonstrated impressive classification performance, attaining an accuracy of 98.33% for normal skin, 98.25% for monkeypox, 84.21% for measles, and 77.27% for chickenpox. Precision, recall, and

Indexed as

Mpox, MonkeypoxSkin DiseasesArtificial IntelligenceChickenpoxHumansMeaslesNeural Networks, ComputerSkinexplainable AIGrad-CAMLIMEmodified Xception modelmonkeypox diseasetransfer learning

Identifiers

PMID41522723
PMCPMC12780539

What OpenQuestion holds

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LicenceCC BY
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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.