State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches...

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Autores principales:	Saqib Ali, Jianqiang Li, Yan Pei, Rooha Khurram, Khalil ur Rehman, Abdul Basit Rasool
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cancer diagnosis machine learning deep learning medical imaging automated computer-aid diagnosis systems Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/71ab4510eb604b7cbdbdef7b78a1d7eb
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id	oai:doaj.org-article:71ab4510eb604b7cbdbdef7b78a1d7eb
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spelling	oai:doaj.org-article:71ab4510eb604b7cbdbdef7b78a1d7eb2021-11-11T15:34:29ZState-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods10.3390/cancers132155462072-6694https://doaj.org/article/71ab4510eb604b7cbdbdef7b78a1d7eb2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5546https://doaj.org/toc/2072-6694Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.Saqib AliJianqiang LiYan PeiRooha KhurramKhalil ur RehmanAbdul Basit RasoolMDPI AGarticlecancer diagnosismachine learningdeep learningmedical imagingautomated computer-aid diagnosis systemsNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5546, p 5546 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cancer diagnosis machine learning deep learning medical imaging automated computer-aid diagnosis systems Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	cancer diagnosis machine learning deep learning medical imaging automated computer-aid diagnosis systems Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Saqib Ali Jianqiang Li Yan Pei Rooha Khurram Khalil ur Rehman Abdul Basit Rasool State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
description	Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.
format	article
author	Saqib Ali Jianqiang Li Yan Pei Rooha Khurram Khalil ur Rehman Abdul Basit Rasool
author_facet	Saqib Ali Jianqiang Li Yan Pei Rooha Khurram Khalil ur Rehman Abdul Basit Rasool
author_sort	Saqib Ali
title	State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
title_short	State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
title_full	State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
title_fullStr	State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
title_full_unstemmed	State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
title_sort	state-of-the-art challenges and perspectives in multi-organ cancer diagnosis via deep learning-based methods
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/71ab4510eb604b7cbdbdef7b78a1d7eb
work_keys_str_mv	AT saqibali stateoftheartchallengesandperspectivesinmultiorgancancerdiagnosisviadeeplearningbasedmethods AT jianqiangli stateoftheartchallengesandperspectivesinmultiorgancancerdiagnosisviadeeplearningbasedmethods AT yanpei stateoftheartchallengesandperspectivesinmultiorgancancerdiagnosisviadeeplearningbasedmethods AT roohakhurram stateoftheartchallengesandperspectivesinmultiorgancancerdiagnosisviadeeplearningbasedmethods AT khalilurrehman stateoftheartchallengesandperspectivesinmultiorgancancerdiagnosisviadeeplearningbasedmethods AT abdulbasitrasool stateoftheartchallengesandperspectivesinmultiorgancancerdiagnosisviadeeplearningbasedmethods
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State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

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