Evidence map›Paper›PMID 41540090›Full record

ArticleScientific reports2026

DermNet: integrative CNN-ViT architecture for bias mitigation in dermatological diagnostics using advanced unsupervised lesion segmentation.

Muhammad Huzaifa Imran, Muhammad Shahid, Mohammad Aazam, Rafia Sajid, Muhammad Aamir Adnan, Khawar Naeem, Amjad Ali

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

7 authors.

Muhammad Huzaifa ImranDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
Muhammad ShahidDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
Mohammad AazamInformation Systems, Carnegie Mellon University, Ar-Rayyan, Qatar. aazam@ieee.org.
Rafia SajidDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
Muhammad Aamir AdnanDepartment of Plastic Surgery, Pak Italian Burn Centre, Nishtar Medical University, Multan, Pakistan.
Khawar NaeemDepartment of Industrial Engineering, University of Engineering & Technology (UET) Peshawar, Peshawar, Pakistan.
Amjad AliFaculty of Science and Engineering, Muscat University, Muscat, Oman. amali@muscatuniversity.edu.om.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we propose a method for reducing the bias in skin disease identification for people of color with the aid of lesion only zero shot unsupervised approach that is then passed to the classifier Dermnet comprising of a hybrid Vision Transformer and Convolutional Neural Network, achieving robust validation accuracy of approximately 81%. Our Segmentation without training with labeled data as is the case with traditional U-Net has achieved an IOU of 90% across all skin colors in segmenting the lesion from skin effectively eradicating the impact of skin in the classification of disease.

Indexed as

Skin DiseasesAlgorithmsConvolutional Neural NetworksHumansNeural Networks, ComputerSkin

Identifiers

PMID41540090
PMCPMC12880972

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.