Efficient framework for brain tumor detection using different deep learning techniques.

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Fatma Taher, Mohamed R Shoaib, Heba M Emara, Khaled M Abdelwahab, Fathi E Abd El-Samie, Mohammad T Haweel

Author Information

Fatma Taher: College of Technological Innovative, Zayed University, Abu Dhabi, United Arab Emirates.
Mohamed R Shoaib: Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
Heba M Emara: Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
Khaled M Abdelwahab: Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
Fathi E Abd El-Samie: Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
Mohammad T Haweel: Department of Electrical Engineering, Shaqra University, Shaqraa, Saudi Arabia.

PMID: 36530682 DOI: 10.3389/fpubh.2022.959667

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-tumor-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-tumor-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-tumor-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the -fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.

CNN MRI brain tumor classification classification deep neural networks pre-trained models segmentation transfer learning

J Ambient Intell Humaniz Comput. 2022;13(9):4477-4492 [PMID: 35280854]
Comput Biol Med. 2019 Aug;111:103345 [PMID: 31279167]
Microsc Res Tech. 2021 Nov;84(11):2504-2516 [PMID: 34121273]
BMC Med Imaging. 2015 Aug 12;15:29 [PMID: 26263899]
Tissue Cell. 2019 Jun;58:76-83 [PMID: 31133249]
Artif Intell Med. 2020 Jan;102:101769 [PMID: 31980106]
Comput Methods Programs Biomed. 2018 Nov;166:33-38 [PMID: 30415716]
IEEE Trans Med Imaging. 2016 Feb;35(2):550-66 [PMID: 26415167]
Front Psychol. 2022 Mar 04;13:848784 [PMID: 35310201]
Med Hypotheses. 2019 Dec;133:109413 [PMID: 31586812]
Semin Neurol. 1986 Mar;6(1):33-42 [PMID: 3332409]
Comput Biol Med. 2019 Oct;113:103387 [PMID: 31421276]
Sci Rep. 2018 Mar 15;8(1):4165 [PMID: 29545529]

Humans

Deep Learning

Brain Neoplasms

Neural Networks, Computer

Brain

Magnetic Resonance Imaging

Journal Article

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