Evidence map›Paper›PMID 40989360›Full record

ArticlePeerJ. Computer science2025

Acute lymphoblastic leukemia cancer diagnosis in children and adults using transforming blood fluorescence microscopy imaging.

Amjad Rehman, Muhammad Mujahid, Tanzila Saba, Faten S Alamri, Noor Ayesha

Abstract read
In one paragraph

Article in PeerJ. Computer science, 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
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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

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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

5 authors.

Amjad RehmanAIDA Lab, CCIS, Prince Sultan University, Riyadh, Saudi Arabia.
Muhammad MujahidAIDA Lab, CCIS, Prince Sultan University, Riyadh, Saudi Arabia.ORCID 0009-0005-5751-5528
Tanzila SabaAIDA Lab, CCIS, Prince Sultan University, Riyadh, Saudi Arabia.
Faten S AlamriDepartment of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Noor AyeshaCenter of Excellence in CyberSecurity (CYBEX), Prince Sultan University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Leukemia is a highly aggressive kind of cancer that may impact the bone marrow. The most fatal type acute lymphoblastic leukemia (ALL), is characterized by the excessive growth of immature white blood cells in the bone marrow. For diagnostic purposes, hematologists and experts use a state-of-the-art microscope fitted with a high-powered magnifying lens to analyze blood and bone marrow samples. Experts attribute the rapid progress to the presence of adolescent white blood cells, not fully developed ones. A good treatment for ALL, no matter where it comes from, includes chemotherapy, medication given through a transplant. Experts have difficulties in accurately evaluating explosive cell features due to the onerous and time-consuming nature of manual diagnosis for this disease. A total of 89 individuals suspected of having ALL underwent sample collection, resulting in the acquisition of 3,256 images. The dataset is classified into four different types of cancer: early-stage, benign cells, pre-cancerous cells, and pro-cancer cells. The proposed approach employs several preprocessing and augmentation techniques to improve the results. The studies demonstrate that the technique achieved a recall rate of 100% for the pro-cell cancer subtype, an overall accuracy of 98.67% using enhanced data, and an overall accuracy of 97.87% using the original data. The experiments have shown that the proposed equipment is superior in reliability and accuracy compared to existing approaches, and it facilitates early detection in medical imaging.

Indexed as

Acute leukemia cancerBlood microscopyHealthcareImage processingMedical informatics

Identifiers

PMID40989360
PMCPMC12453865

What OpenQuestion holds

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LicenceCC BY
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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.