Open Access

ARTICLE

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

M. Uvaneshwari¹, M. Baskar^2,*

1 Department of Computer Science and Engineering, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, 603203, Tamilnadu, India
2 Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, 603203, Tamilnadu, India

* Corresponding Author: M. Baskar. Email:

Computer Systems Science and Engineering 2023, 46(2), 1811-1826. https://doi.org/10.32604/csse.2023.035455

Received 22 August 2022; Accepted 14 December 2022; Issue published 09 February 2023

Abstract

The Brain Tumor (BT) is created by an uncontrollable rise of anomalous cells in brain tissue, and it consists of 2 types of cancers they are malignant and benign tumors. The benevolent BT does not affect the neighbouring healthy and normal tissue; however, the malignant could affect the adjacent brain tissues, which results in death. Initial recognition of BT is highly significant to protecting the patient’s life. Generally, the BT can be identified through the magnetic resonance imaging (MRI) scanning technique. But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain. Recently, ML has prevailed against standard image processing techniques. Several studies denote the superiority of machine learning (ML) techniques over standard techniques. Therefore, this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning (BTDC-MOML) model. To accomplish the detection of brain tumor effectively, a Computer-Aided Design (CAD) model using Machine Learning (ML) technique is proposed in this research manuscript. Initially, the input image pre-processing is performed using Gaborfiltering (GF) based noise removal, contrast enhancement, and skull stripping. Next, mayfly optimization with the Kapur’s thresholding based segmentation process takes place. For feature extraction proposes, local diagonal extreme patterns (LDEP) are exploited. At last, the Extreme Gradient Boosting (XGBoost) model can be used for the BT classification process. The accuracy analysis is performed in terms of Learning accuracy, and the validation accuracy is performed to determine the efficiency of the proposed research work. The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

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