Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate...

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Autores principales:	Ahsan Bin Tufail, Yong-Kui Ma, Mohammed K. A. Kaabar, Francisco Martínez, A. R. Junejo, Inam Ullah, Rahim Khan
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/e825880cb8cf4c5e90472ca1ffb55f45
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spelling	oai:doaj.org-article:e825880cb8cf4c5e90472ca1ffb55f452021-11-08T02:35:46ZDeep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions1748-671810.1155/2021/9025470https://doaj.org/article/e825880cb8cf4c5e90472ca1ffb55f452021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9025470https://doaj.org/toc/1748-6718Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.Ahsan Bin TufailYong-Kui MaMohammed K. A. KaabarFrancisco MartínezA. R. JunejoInam UllahRahim KhanHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Ahsan Bin Tufail Yong-Kui Ma Mohammed K. A. Kaabar Francisco Martínez A. R. Junejo Inam Ullah Rahim Khan Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
description	Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.
format	article
author	Ahsan Bin Tufail Yong-Kui Ma Mohammed K. A. Kaabar Francisco Martínez A. R. Junejo Inam Ullah Rahim Khan
author_facet	Ahsan Bin Tufail Yong-Kui Ma Mohammed K. A. Kaabar Francisco Martínez A. R. Junejo Inam Ullah Rahim Khan
author_sort	Ahsan Bin Tufail
title	Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
title_short	Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
title_full	Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
title_fullStr	Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
title_full_unstemmed	Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
title_sort	deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/e825880cb8cf4c5e90472ca1ffb55f45
work_keys_str_mv	AT ahsanbintufail deeplearningincancerdiagnosisandprognosispredictionaminireviewonchallengesrecenttrendsandfuturedirections AT yongkuima deeplearningincancerdiagnosisandprognosispredictionaminireviewonchallengesrecenttrendsandfuturedirections AT mohammedkakaabar deeplearningincancerdiagnosisandprognosispredictionaminireviewonchallengesrecenttrendsandfuturedirections AT franciscomartinez deeplearningincancerdiagnosisandprognosispredictionaminireviewonchallengesrecenttrendsandfuturedirections AT arjunejo deeplearningincancerdiagnosisandprognosispredictionaminireviewonchallengesrecenttrendsandfuturedirections AT inamullah deeplearningincancerdiagnosisandprognosispredictionaminireviewonchallengesrecenttrendsandfuturedirections AT rahimkhan deeplearningincancerdiagnosisandprognosispredictionaminireviewonchallengesrecenttrendsandfuturedirections
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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

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