Open Access

ARTICLE

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

by Muhammad Irfan¹, Ahmad Shaf^2,*, Tariq Ali², Umar Farooq², Saifur Rahman¹, Salim Nasar Faraj Mursal¹, Mohammed Jalalah¹, Samar M. Alqhtani³, Omar AlShorman⁴

1 Electrical Engineering Department, College of Engineering, Najran University, Najran, 61441, Saudi Arabia
2 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
3 Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
4 College of Engineering, Najran University, Najran, 61441, Saudi Arabia

* Corresponding Author: Ahmad Shaf. Email:

(This article belongs to the Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)

Computers, Materials & Continua 2023, 76(1), 711-729. https://doi.org/10.32604/cmc.2023.038176

Received 30 November 2022; Accepted 12 April 2023; Issue published 08 June 2023

Abstract

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

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Keywords

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