Artificial intelligence for cancer diagnosis

Khalil, Sura; Jaber, Mustafa Musa; Ali, Sarah Yahya; Ali, Mohammed Hasan

doi:10.1088/978-0-7503-3595-9ch10

Cited by 1 publication

(2 citation statements)

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“…ML can conduct complex tasks, such as introducing a human-like interpretation of an image [ 9 ]. ML, and in particular DL, has been incorporated into lung cancer research.…”

Section: Introductionmentioning

confidence: 99%

“…ML has the potential to analyze and identify specific types of cancer, predict cancer susceptibility, and screen individuals for cancer regularly before it can spread. ML models have also been developed to help doctors conduct CT scans and project images of cancer [ 9 ]. DNA strains and variants that indicate early-stage risk factors for cancer and other disorders have been identified with the help of ML [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics

Kuang,

Kouznetsova,

Kesari

et al. 2023

Metabolites

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The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications’ accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model’s accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.

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“…ML can conduct complex tasks, such as introducing a human-like interpretation of an image [ 9 ]. ML, and in particular DL, has been incorporated into lung cancer research.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%