Evidence map›Paper›PMID 37346565›Full record

ArticlePeerJ. Computer science2023

A novel artificial intelligence-based predictive analytics technique to detect skin cancer.

Prasanalakshmi Balaji, Bui Thanh Hung, Prasun Chakrabarti, Tulika Chakrabarti, Ahmed A Elngar, Rajanikanth Aluvalu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2023. 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

6 authors.

Prasanalakshmi BalajiData Science Laboratory, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Vietnam.ORCID 0000-0002-6882-2233
Bui Thanh HungData Science Laboratory, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Vietnam.
Prasun ChakrabartiITM (SLS) Baroda University, Vadodara, Gujarat, India.
Tulika ChakrabartiSir Padamapat Singhania University, Udaipur, Rajasthan, India.
Ahmed A ElngarFaculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
Rajanikanth AluvaluDepartment of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India.ORCID 0000-0001-8508-6066

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage. More than 75% of fatalities worldwide are related to skin cancer. A novel Artificial Golden Eagle-based Random Forest (AGEbRF) is created in this study to predict skin cancer cells at an early stage. Dermoscopic images are used in this instance as the dataset for the system's training. Additionally, the dermoscopic image information is processed using the established AGEbRF function to identify and segment the skin cancer-affected area. Additionally, this approach is simulated using a Python program, and the current research's parameters are assessed against those of earlier studies. The results demonstrate that, compared to other models, the new research model produces better accuracy for predicting skin cancer by segmentation.

Indexed as

Artificial intelligenceDeep learningMachine learningMalignant tumorsSkin cancer

Identifiers

PMID37346565
PMCPMC10280503

What OpenQuestion holds

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LicenceCC BY
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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.