Open Access

ARTICLE

Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

by Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*

1 Department of Electrical Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, 91431, Saudi Arabia

* Corresponding Author: Ahmed Alsheikhy. Email:

(This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)

Computers, Materials & Continua 2023, 77(1), 425-443. https://doi.org/10.32604/cmc.2023.040561

Received 23 March 2023; Accepted 07 August 2023; Issue published 31 October 2023

Abstract

According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved exquisite results. This study proposes a new Computer-Aided Diagnosis (CAD) system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered tumors. The segmentation mechanism is used to determine the shape, area, diameter, and outline of any tumors, while the classification mechanism categorizes the type of cancer as slow-growing or aggressive. The main goal is to diagnose tumors early and to support the work of physicians. The proposed system integrates a Convolutional Neural Network (CNN), VGG-19, and Long Short-Term Memory Networks (LSTMs). A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of tumors. Numerous experiments have been conducted on different five datasets to evaluate the presented system. These experiments reveal that the system achieves 97.98% average accuracy when the segmentation and classification functions were utilized, demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI images. In addition, the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’ lives and avoid the high cost of treatments.

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Keywords

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