Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study

Hilali-Jaghdam, Inès; Ishak, Anis Ben; Abdel‐Khalek, S.; Jamal, Amani

doi:10.1016/j.comcom.2020.08.010

Cited by 32 publications

(11 citation statements)

References 32 publications

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“…When the genetic operators are processed, the chromosomes in the population disappear, and the population number is fixed with the addition of the newly formed chromosomes. Segmentation of medical images [27], reallocation of load balances of virtual machines between resources [28], has been used by GA, Higazy, and Alyami [29] to model another current problem, COVID-19 outbreak transmission. GA has also been used in optimization [30] and classification problems [31].…”

Section: Materials and Methods 21 Genetic Algorithmmentioning

confidence: 99%

Epigenetik Algoritma ile Konferans Çizelgeleme

Atagün

Biroğul

2024

Politeknik Dergisi

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Bilimsel çalışmaların sunumlarının yapıldığı etkinliklerin en önemlileri akademik konferanslardır. Multidisipliner konferanslar düzenlemek için günler, salonlar ve oturumlar önceden belirlenir ve bu sürece konferans çizelgeleme denir. Multidisipliner konferanslarda, sunumların planlanmasında aynı alanlara ait çalışmaların aynı oturumlarda bir arada bulunması konferans dinleyicisi ve konferans konuşmacısı için çok önemlidir. Bu bağlamda multidisipliner konferans planlama problemi çok kısıtlı bir optimizasyon problemi olarak görülmektedir. Çok kısıtlı optimizasyon problemleri, geleneksel optimizasyon yöntemleriyle değil, sezgisel optimizasyon teknikleriyle çözülür. Bu çalışmada, multidisipliner konferans verisi kullanılarak konferans planlaması sorunu ele alınmaktadır. Konferans çizelgeleme probleminin çözümü Genetik Algoritma (GA) ve Epigenetik Algoritma (EGA) ile C# programlama dili kullanılarak gerçekleştirilmiştir. Çalışmada GA ve EGA ile elde edilen deneysel sonuçlar incelenmiştir. Bu inceleme sonucunda EGA'nın klasik GA'ya göre daha az iterasyonda daha iyi sonuçlar elde ettiği görülmüştür.

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Section: Materials and Methods 21 Genetic Algorithmmentioning

confidence: 99%

Epigenetik Algoritma ile Konferans Çizelgeleme

Atagün

Biroğul

2024

Politeknik Dergisi

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“…For comparative purposes, the Particle Swarm Optimization method (PSO) was also applied. It has been demonstrated that in the optimization job, the QGA exceeds the GA and the PSO greatly outperforms both methods [27]. Intelligent Fuzzy Level Set Method (IFLSM) and Improved Quantum Particle Swarm Optimization (IQPSO) are presented for picture segmentation.…”

Section: Engineering Applications Of Artificial Intelligencementioning

confidence: 99%

Image Segmentation Using Hybrid Optimization Algorithms: Review

Hemeida¹,

Yahya

Al-Sanary

2022

Aswan University Journal of Sciences and Technology

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Image segmentation, often based on the properties of image pixels, is a widely used method in digital image processing and analysis to divide an image into multiple parts or areas. The main goal of image segmentation is to simplify the image for easier analysis. It would be very difficult to implement computer vision without performing image segmentation. An important component of computer vision is image segmentation, which has numerous commercial applications. Google and others Image-based search engines use image segmentation algorithms to identify the objects in your image and match their results to the relevant images they find to present you with search results.

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“…At the current age, the legitimacy of each person from the general population is overviewed. The best people are picked subject to their wellbeing and modified (reintegrated and possibly changed) to shape another new people 29 . Figure 2 represents the basic steps involved in the GA process.…”

Section: Basic Preliminariesmentioning

confidence: 99%

An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer

Jha

Mehta

2021

Concurrency and Computation

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Summary In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose this malignancy at an early stage. The objective of this research is to mix machine learning and soft computing techniques to gain higher accuracy within the prediction of SC. To play out the exploration work, we utilized two data sets, one from “Save Life Hospital,” India, and the other is the UCI repository skin cancer data set. In this article, three meta‐heuristic algorithms, the FS_GA, the FS_PSO, and the FS_ACO, were used to select the best features from the data set provided to it. The AFRG_algorithm generates a set of fuzzy rules automatically and the RR_algorithm reduces certain fuzzy rules from the fuzzy system. For the SCC_dataset, the end accuracy obtained was 97.67%, 98.45%, and 99.22%. For the UCI_dataset, the end accuracy obtained was 98.81%, 99.72%, and 99.67%. Experimental results on the used datasets show that the proposed method strikingly improves the forecast exactitude of skin malignancy.

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Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study

Cited by 32 publications

References 32 publications

Epigenetik Algoritma ile Konferans Çizelgeleme

Epigenetik Algoritma ile Konferans Çizelgeleme

Image Segmentation Using Hybrid Optimization Algorithms: Review

An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer

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