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DeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor

Authors :
Muhammad Yaqub
Feng Jinchao
Shahzad Ahmed
Atif Mehmood
Imran Shabir Chuhan
Malik Abdul Manan
Muhammad Salman Pathan
Source :
Alexandria Engineering Journal, Vol 76, Iss , Pp 609-627 (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Brain tumors, which are uncontrolled growths of brain cells, pose a threat to people worldwide. However, accurately classifying brain tumors through computerized methods has been difficult due to differences in size, shape, and location of the tumors and limitations in the medical field. Improved precision is critical in detecting brain tumors, as small errors in human judgments can result in increased mortality rates. This paper proposes a new method for improving early detection and decision-making in brain tumor severity using learning methodologies. Clinical datasets are used to obtain benchmark images of brain tumors, which undergo pre-processing, data augmentation with a Generative Adversarial Network, and classification with an Adaptive Layer Cascaded ResNet (ALCResNet) optimized with Improved Border Collie Optimization (IBCO). The abnormal images are then segmented using the DeepLabV3 model and fed into the ALCResNet for final classification into Meningioma, Glioma, or Pituitary. The IBCO algorithm-based ALCResNet model outperforms other heuristic classifiers for brain tumor classification and severity estimation, with improvements ranging from 1.3% to 4.4% over COA-ALCResNet, DHOA-ALCResNet, MVO-ALCResNet, and BCO-ALCResNet. The IBCO algorithm-based ALCResNet model also achieves higher accuracy than non-heuristic classifiers such as CNN, DNN, SVM, and ResNet, with improvements ranging from 2.4% to 3.6% for brain tumor classification and 0.9% to 3.8% for severity estimation. The proposed method offers an automated classification and grading system for brain tumors and improves the accuracy of brain tumor classification and severity estimation, promoting more precise decision-making regarding diagnosis and treatment.

Details

Language :
English
ISSN :
11100168
Volume :
76
Issue :
609-627
Database :
Directory of Open Access Journals
Journal :
Alexandria Engineering Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.27885af96d8245f6945c5d93c69201f3
Document Type :
article
Full Text :
https://doi.org/10.1016/j.aej.2023.06.062