Intelligent Model for Brain Tumor Identification Using Deep Learning

Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more effective in tumor healing. The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The clas...

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Vydáno v:	Applied Computational Intelligence and Soft Computing Ročník 2022; s. 1 - 10
Hlavní autoři:	Khan, Abdul Hannan, Abbas, Sagheer, Khan, Muhammad Adnan, Farooq, Umer, Khan, Wasim Ahmad, Siddiqui, Shahan Yamin, Ahmad, Aiesha
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Hindawi 21.01.2022 John Wiley & Sons, Inc Wiley
Témata:	Artificial neural networks Brain Brain cancer Brain tumors Deep learning Gliomas Image classification Image processing Image segmentation Machine learning Magnetic resonance imaging Medical imaging Neural networks Physicians Polyps Tumors Pakistan
ISSN:	1687-9724, 1687-9732
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Abstract	Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more effective in tumor healing. The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The classification of brain tumors is a significant part that depends on the expertise and knowledge of the physician. An intelligent system for detecting and classifying brain tumors is essential to help physicians. The novel feature of the study is the division of brain tumors into glioma, meningioma, and pituitary using a hierarchical deep learning method. The diagnosis and tumor classification are significant for the quick and productive cure, and medical image processing using a convolutional neural network (CNN) is giving excellent outcomes in this capacity. CNN uses the image fragments to train the data and classify them into tumor types. Hierarchical Deep Learning-Based Brain Tumor (HDL2BT) classification is proposed with the help of CNN for the detection and classification of brain tumors. The proposed system categorizes the tumor into four types: glioma, meningioma, pituitary, and no-tumor. The suggested model achieves 92.13% precision and a miss rate of 7.87%, being superior to earlier methods for detecting and segmentation brain tumors. The proposed system will provide clinical assistance in the area of medicine.
AbstractList	Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more effective in tumor healing. The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The classification of brain tumors is a significant part that depends on the expertise and knowledge of the physician. An intelligent system for detecting and classifying brain tumors is essential to help physicians. The novel feature of the study is the division of brain tumors into glioma, meningioma, and pituitary using a hierarchical deep learning method. The diagnosis and tumor classification are significant for the quick and productive cure, and medical image processing using a convolutional neural network (CNN) is giving excellent outcomes in this capacity. CNN uses the image fragments to train the data and classify them into tumor types. Hierarchical Deep Learning-Based Brain Tumor (HDL2BT) classification is proposed with the help of CNN for the detection and classification of brain tumors. The proposed system categorizes the tumor into four types: glioma, meningioma, pituitary, and no-tumor. The suggested model achieves 92.13% precision and a miss rate of 7.87%, being superior to earlier methods for detecting and segmentation brain tumors. The proposed system will provide clinical assistance in the area of medicine.
Audience	Academic
Author	Ahmad, Aiesha Abbas, Sagheer Khan, Wasim Ahmad Khan, Abdul Hannan Khan, Muhammad Adnan Farooq, Umer Siddiqui, Shahan Yamin
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Cites_doi	10.1155/2021/5544742 10.1007/s10916-018-1094-3 10.1109/EIT.2018.8500308 10.5815/ijem.2019.01.02 10.1109/access.2019.2904145 10.31803/tg-20190712095507 10.1016/j.bspc.2019.101728 10.1016/j.neunet.2019.03.014 10.1088/1742-6596/1916/1/012226 10.1016/j.bspc.2017.07.007 10.18383/j.tom.2019.00026 10.1155/2017/9545920 10.1109/ICICCT.2018.8473297 10.1007/s11042-019-7419-5 10.1109/tnnls.2018.2796023 10.1016/j.patrec.2019.11.020 10.1016/j.mehy.2020.109696
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Copyright	Copyright © 2022 Abdul Hannan Khan et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Abdul Hannan Khan et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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Snippet	Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more...
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SubjectTerms	Artificial neural networks Brain Brain cancer Brain tumors Deep learning Gliomas Image classification Image processing Image segmentation Machine learning Magnetic resonance imaging Medical imaging Neural networks Physicians Polyps Tumors
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Title	Intelligent Model for Brain Tumor Identification Using Deep Learning
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