Open Access

ARTICLE

An Effective Diagnosis System for Brain Tumor Detection and Classification

Ahmed A. Alsheikhy^1,*, Ahmad S. Azzahrani¹, A. Khuzaim Alzahrani², Tawfeeq Shawly³

1 Electrical Engineering Department, College of Engineering, Northern Border University, Arar, 91431, Saudi Arabia
2 Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Northern Border University, Arar, 91431, Saudi Arabia
3 Electrical Engineering Department, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Ahmed A. Alsheikhy. Email:

Computer Systems Science and Engineering 2023, 46(2), 2021-2037. https://doi.org/10.32604/csse.2023.036107

Received 17 September 2022; Accepted 08 December 2022; Issue published 09 February 2023

Abstract

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a Support Vector Machine (SVM). Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.

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Keywords

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