Research Article Improved Machine Learning Method for Intracranial Tumor Detection with Accelerated Particle Swarm Optimization K. R. Pradeep , 1 Syam Machinathu Parambil Gangadharan , 2 Wesam Atef Hatamleh, 3 Hussam Tarazi, 4 Piyush Kumar Shukla , 5 and Basant Tiwari 6 1 Department of Computer Science & Engineering, B.M.S Institute of Technology and Management, Avalahalli, Bengaluru 560064, India 2 General Mills, 220 Carlson Parkway, Apt 208, Minnetonka 55305, Minnesota, USA 3 Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia 4 Department of Computer Science and Informatics, School of Engineering and Computer Science, Oakland University, Rochester Hills, 318 Meadow Brook Rd, Rochester 48309, MI, USA 5 Department of Computer Science & Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal 462033, Madhya Pradesh, India 6 Department of Computer Science, Hawassa University, Awasa, Ethiopia Correspondence should be addressed to Basant Tiwari; basanttiw@hu.edu.et Received 15 November 2021; Accepted 24 January 2022; Published 3 March 2022 Academic Editor: Bhagyaveni M.A Copyright © 2022 K. R. Pradeep et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e field of image processing is distinguished by the variety of functions it offers and the wide range of applications it has in biomedical imaging. It becomes a difficult and time-consuming process for radiologists to do the manual identification and categorization of the tumour. It is a complex and time-consuming procedure conducted by radiologists or clinical professionals to remove the contaminated tumour region from magnetic resonance (MR) pictures. It is the goal of this study to improve the performance and reduce the complexity of the image segmentation process by investigating FCM predicted image segmentation procedures in order to reduce the intricacy of the process. Furthermore, relevant characteristics are collected from each segmented tissue and aligned as input to the classifiers for autonomous identification and relegation of encephalon cancers in order to increase the accuracy and quality rate of the neural network classifier. An evaluation, validation, and presentation of the ex- perimental performance of the suggested approach have been completed. A unique APSO (accelerated particle swarm opti- mization) based artificial neural network model (ANNM) for the relegation of benign and malignant tumours is presented in this study effort, which allows for the automated identification and categorization of brain tumours. Using APSO training to improve the suggested ANNM model parameters would give a unique method to alleviate the stressful work of radiologists performing manual identification of encephalon cancers from MR images. e use of an APSO-based ANNM (artificial neural network model) model for automated brain tumour classification has been presented in order to demonstrate the resilience of the classification model. It has been suggested to utilise the improved enhanced fuzzy c means (IEnFCM) method for image segmentation, while the GLCM (gray level co-occurrence matrix) feature extraction approach has been employed for feature extraction from magnetic resonance imaging (MR pictures). 1. Introduction In the current medical sciences and clinical research de- velopments, many models have been developed for earlier tumor detection for saving a patient’s life. Perhaps, detecting tumor from MRI scans is a very time-consuming and complicated process. It takes several times for segmenting the tumor cells from brain tissues [1]. Consequently, image processing techniques have been incorporated for the tumor detection process in recent times, which provides the results Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 1128217, 13 pages https://doi.org/10.1155/2022/1128217