Evidence map›Paper›PMID 42140962›Full record

ArticleScientific reports2026

Artificial intelligence with deep learning driven entropy-curvature attention mechanism for detection and segmentation of skin lesions using biomedical images.

Tawfiq Hasanin, Mona Almofarreh, Hend Khalid Alkahtani, Mashael M Asiri, Faisal Mohammed Nafie, Mohammed Alahmadi, Adel Albshri, Abdulsamad Ebrahim Yahya

Abstract read
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Tawfiq HasaninDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Mona AlmofarrehDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh, 11543, Saudi Arabia.
Hend Khalid AlkahtaniDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Mashael M AsiriDepartment of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia.
Faisal Mohammed NafieEngineering and Technology Unit, Applied College, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
Mohammed AlahmadiDepartment of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21493, Saudi Arabia.
Adel AlbshriDepartment of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21493, Saudi Arabia.
Abdulsamad Ebrahim YahyaDepartment of Information Technology, College of Computing and Information Technology, Northern Border University, Arar, Saudi Arabia. Abdulsamad.qasem@nbu.edu.sa.

Funding

Princess Nourah bint Abdulrahman University PNURSP2025Rxxx
6 · The paper itself

Abstract

In medical image analysis, skin lesion diagnosis remains a complicated task. Skin lesions are a prevalent form of skin disease that exists globally. A malignant skin cancer, otherwise called melanoma, usually starts after melanocyte cells begin to grow uncontrollably. Among several forms of cancer, malignant melanoma is the deadliest form as it has a high vulnerability to spread into deeper tissues and because of its high death rate as well. Early identification of skin tumours is essential to permit advanced treatment. In general, the manual inspection of dermatologists is time-consuming, subjective, and tedious, resulting in dissimilar recognition precision based on their experience. In recent times, many investigators have studied the application of deep learning (DL) methods to skin lesion segmentation. In this study, the DL-driven entropy-curvature attention mechanism for enhanced segmentation and classification of skin lesions (DLECAM-ESCSL) model is proposed in medical imaging techniques. The primary purpose of the DLECAM-ESCSL model is to develop an effective approach for precise skin lesion segmentation to assist in early and reliable skin disease diagnosis using advanced techniques. At first, the image pre-processing step involves various levels, such as image resizing, hair removal, and noise removal, to improve the quality of raw images by eliminating the noise. Furthermore, the attention mechanism-based entropy-curvature (ECA) method is employed for the segmentation process. For the feature extraction process, the DLECAM-ESCSL model utilises the vision transformer (ViT) model to recognise and isolate the most relevant information from raw data. Finally, the Wasserstein autoencoder (WAE) model is used for classification. The performance valuation of the DLECAM-ESCSL approach portrayed a superior accuracy value of 99.16% over existing methods under the Skin Cancer ISIC dataset.

Indexed as

Artificial IntelligenceDeep LearningImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMelanomaSkinSkin NeoplasmsAlgorithmsEntropyHumansDeep learningEntropy–curvature attention mechanismSegmentationSkin lesionVision transformer

Identifiers

PMID42140962
PMCPMC13370022

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.