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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Vydané v:Applied Computational Intelligence and Soft Computing Ročník 2022; s. 1 - 10
Hlavní autori: Khan, Abdul Hannan, Abbas, Sagheer, Khan, Muhammad Adnan, Farooq, Umer, Khan, Wasim Ahmad, Siddiqui, Shahan Yamin, Ahmad, Aiesha
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Hindawi 21.01.2022
John Wiley & Sons, Inc
Wiley
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1687-9724
1687-9732
DOI:10.1155/2022/8104054