EWPCO-enabled Shepard convolutional neural network for classification of brain tumour using MRI image

• Published in: The imaging science journal Vol. ahead-of-print; no. ahead-of-print; pp. 1 - 18
• Main Authors: Mohana Sundaram, K., Sasikumar, R.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 02.04.2024
• Subjects: Chimp Optimization Algorithm (ChOA); Computerized Tomography (CT); deep learning (DL); Exponential Weighted Pelican Chimp Optimization (EWPCO); Magnetic resonance imaging (MRI); Pelican Optimization Algorithm (POA); Pyramid Scene Parsing Network (PSP-Net); Region of Interest (RoI)
• ISSN: 1368-2199; 1743-131X
• DOI: 10.1080/13682199.2023.2206271

Numerous imaging techniques, like X-rays, Computerized Tomography (CT) scans, and ultrasound are utilized to predict brain tumours, but these imaging techniques experience difficulties in generating accurate results. To overcome such limitations, an effectual approach for the classification of brain cancer utilizing the proposed Exponentially Weighted Pelican Chimp Optimization-based Shepard Convolutional Neural Network (EWPCO-ShCNN) is introduced. At first, preprocessing is carried out employing a median filter, and Region of Interest (RoI) extraction and segmentation are performed utilizing a Pyramid Scene Parsing Network (PSP-Net), which is trained by Pelican Chimp Optimization (PCO) algorithm. After that, data augmentation and feature extraction are performed for more processing. Thereafter, the categorization is executed by ShCNN, which is instructed by the proposed Exponential Weighted Pelican Chimp Optimization (EWPCO) algorithm. Furthermore, the proposed EWPCO-ShCNN has attained better sensitivity of 95.90%, accuracy of 94.90% and specificity of 95.60% respectively.