A Selected Deep Learning Cancer Prediction Framework

Deep learning (DL) algorithms are crucial for predicting various diseases because they can analyze a large amount of healthcare data within a short prediction time. One of these diseases is cancer, which causes one out of six deaths worldwide. Many researchers have adopted predictive frameworks such...

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Autores principales:	Nadia G. Elseddeq, Sally M. Elghamrawy, Mofreh M. Salem, Ali I. Eldesouky
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	BACP-WOA-S cancer diagnosis deep learning exploitation exploration feature selection Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8f3162fbbafd48d991f9f4387c2b7c87
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id	oai:doaj.org-article:8f3162fbbafd48d991f9f4387c2b7c87
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spelling	oai:doaj.org-article:8f3162fbbafd48d991f9f4387c2b7c872021-11-17T00:00:40ZA Selected Deep Learning Cancer Prediction Framework2169-353610.1109/ACCESS.2021.3124889https://doaj.org/article/8f3162fbbafd48d991f9f4387c2b7c872021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598853/https://doaj.org/toc/2169-3536Deep learning (DL) algorithms are crucial for predicting various diseases because they can analyze a large amount of healthcare data within a short prediction time. One of these diseases is cancer, which causes one out of six deaths worldwide. Many researchers have adopted predictive frameworks such as machine learning and DL to predict cancer prognosis, in addition to the probability of its recurrence, progression, and the patients’ survival estimation. Currently, all stakeholders are interested in the accuracy of cancer prognosis prediction. This study selected a framework within high accuracy and short prediction time from three DL frameworks for improving the performance of cancer prognosis prediction. This prediction requires a quick and high-accuracy optimizer, so we propose a binary version of the continuous AC-parametric whale optimization algorithm. This version is built on S-shaped transfer functions to identify the minimal optimal subset of features and maximize the classification accuracy. These frameworks proposed have the following forms: the first is a Feed-Forward Neural Network (FFNN) in which the input is the optimal set of feature selection. The second is an optimized parameter FFNN. The third is composed of a feature selection layer in which the best subset of selected features is for use as inputs in the optimized FFNN. We compared these frameworks using a comparative study. Our results show that, under all conditions, the third framework is superior to the others with an average accuracy of 100%, whereas the first and second frameworks achieved 94.97% and 93.12% accuracy, respectively.Nadia G. ElseddeqSally M. ElghamrawyMofreh M. SalemAli I. EldesoukyIEEEarticleBACP-WOA-Scancer diagnosisdeep learningexploitationexplorationfeature selectionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151476-151492 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	BACP-WOA-S cancer diagnosis deep learning exploitation exploration feature selection Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	BACP-WOA-S cancer diagnosis deep learning exploitation exploration feature selection Electrical engineering. Electronics. Nuclear engineering TK1-9971 Nadia G. Elseddeq Sally M. Elghamrawy Mofreh M. Salem Ali I. Eldesouky A Selected Deep Learning Cancer Prediction Framework
description	Deep learning (DL) algorithms are crucial for predicting various diseases because they can analyze a large amount of healthcare data within a short prediction time. One of these diseases is cancer, which causes one out of six deaths worldwide. Many researchers have adopted predictive frameworks such as machine learning and DL to predict cancer prognosis, in addition to the probability of its recurrence, progression, and the patients’ survival estimation. Currently, all stakeholders are interested in the accuracy of cancer prognosis prediction. This study selected a framework within high accuracy and short prediction time from three DL frameworks for improving the performance of cancer prognosis prediction. This prediction requires a quick and high-accuracy optimizer, so we propose a binary version of the continuous AC-parametric whale optimization algorithm. This version is built on S-shaped transfer functions to identify the minimal optimal subset of features and maximize the classification accuracy. These frameworks proposed have the following forms: the first is a Feed-Forward Neural Network (FFNN) in which the input is the optimal set of feature selection. The second is an optimized parameter FFNN. The third is composed of a feature selection layer in which the best subset of selected features is for use as inputs in the optimized FFNN. We compared these frameworks using a comparative study. Our results show that, under all conditions, the third framework is superior to the others with an average accuracy of 100%, whereas the first and second frameworks achieved 94.97% and 93.12% accuracy, respectively.
format	article
author	Nadia G. Elseddeq Sally M. Elghamrawy Mofreh M. Salem Ali I. Eldesouky
author_facet	Nadia G. Elseddeq Sally M. Elghamrawy Mofreh M. Salem Ali I. Eldesouky
author_sort	Nadia G. Elseddeq
title	A Selected Deep Learning Cancer Prediction Framework
title_short	A Selected Deep Learning Cancer Prediction Framework
title_full	A Selected Deep Learning Cancer Prediction Framework
title_fullStr	A Selected Deep Learning Cancer Prediction Framework
title_full_unstemmed	A Selected Deep Learning Cancer Prediction Framework
title_sort	selected deep learning cancer prediction framework
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8f3162fbbafd48d991f9f4387c2b7c87
work_keys_str_mv	AT nadiagelseddeq aselecteddeeplearningcancerpredictionframework AT sallymelghamrawy aselecteddeeplearningcancerpredictionframework AT mofrehmsalem aselecteddeeplearningcancerpredictionframework AT aliieldesouky aselecteddeeplearningcancerpredictionframework AT nadiagelseddeq selecteddeeplearningcancerpredictionframework AT sallymelghamrawy selecteddeeplearningcancerpredictionframework AT mofrehmsalem selecteddeeplearningcancerpredictionframework AT aliieldesouky selecteddeeplearningcancerpredictionframework
_version_	1718426062531067904

A Selected Deep Learning Cancer Prediction Framework

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