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AGWO-CNN Classification for Computer-Assisted Diagnosis of Brain Tumors

T. Jeslin1,*, J. Arul Linsely2

1 Department of Electronics and Communication Engineering, Universal College of Engineering and Technology, Vallioor, 627117, India
2 Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumarakovil, 629180, India

* Corresponding Author: T. Jeslin. Email: email

(This article belongs to the Special Issue: Advanced signal acquisition and processing for Internet of Medical Things)

Computers, Materials & Continua 2022, 71(1), 171-182. https://doi.org/10.32604/cmc.2022.020255

Abstract

Brain cancer is the premier reason for cancer deaths all over the world. The diagnosis of brain cancer at an initial stage is mediocre, as the radiologist is ineffectual. Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful. Therefore, the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules. The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region. In the beginning stage, an anisotropic diffusion filters (ADF) method with un-sharp intensification masking is utilized to eliminate the noise discernment in images. In the following stage, within the improved nodule image sequence, a Superpixel Segmentation Based Iterative Clustering (SSBIC) algorithm is proposed for irregular brain tissue prediction. Subsequently, the brain nodule samples are captured using deep learning methods: Advanced Grey Wolf Optimization (AGWO) with ONN (AGWO-ONN) and Advanced GWO with CNN-based (AGWO-CNN). The proposed technique indicates that the sensitivity is increased and the calculation time is decreased. Consequently, the proposed methodology manifests that the advanced Computer-Assisted Diagnosis (CAD) system has outstanding potential for automatic brain tumor diagnosis. The average segmentation time of the nodule slice order is 1.06s, and 97% of AGWO-ONN and 97.6% of AGWO-CNN achieve the best classification reliability.

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APA Style
Jeslin, T., Linsely, J.A. (2022). AGWO-CNN classification for computer-assisted diagnosis of brain tumors. Computers, Materials & Continua, 71(1), 171-182. https://doi.org/10.32604/cmc.2022.020255
Vancouver Style
Jeslin T, Linsely JA. AGWO-CNN classification for computer-assisted diagnosis of brain tumors. Comput Mater Contin. 2022;71(1):171-182 https://doi.org/10.32604/cmc.2022.020255
IEEE Style
T. Jeslin and J.A. Linsely, "AGWO-CNN Classification for Computer-Assisted Diagnosis of Brain Tumors," Comput. Mater. Contin., vol. 71, no. 1, pp. 171-182. 2022. https://doi.org/10.32604/cmc.2022.020255



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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