Open Access Open Access  Restricted Access Subscription or Fee Access

Brain Tumor Detection from MRI Images Using Artificial Intelligence


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irea.v10i3.21213

Abstract


The accumulation of mass proliferation of irregular cells in the brain is known as a tumor. Since brain tumors are rare and come in a variety of forms, predicting the survival rate of a tumor-prone patient is challenging. People are now dying because of brain tumors. As a result, the mortality rate from brain tumors can be reduced if they are detected early and accurately. Treatment for a brain tumor is determined by several factors, including the type of tumor, the abnormality of the cells, and the location of the tumor in the brain. Deep Fully Convolutional Networks (FCNs) has been used to diagnose brain tumors using Magnetic Resonance Imaging Images (MRIs) in this article. As a proof of concept, the approach has been 97.67 percent accurate and 97.67 percent sensitive.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords


Brain Tumor Detection; Image Processing; Deep Learning Networks; Fully Convolutional Networks; OpenCV; MRI Images

Full Text:

PDF


References


G. Garg, and R. Garg, Brain Tumor Detection and Classification based on Hybrid Ensemble Classifier, (2021) arXiv preprint arXiv:2101.00216.
https://doi.org/10.1201/9781003150664-27

Koma'rudin, N., Zakaria, Z., Soh, P., Lago, H., Alsariera, H., Hassan, N., A Review of Recent Microwave Breast Imaging, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (4), pp. 257-267.
https://doi.org/10.15866/irecap.v10i4.18560

Bataineh, A., Batayneh, W., Harahsheh, T., Hijazi, K., Alrayes, A., Olimat, M., Bataineh, A., Early Detection of Cardiac Diseases from Electrocardiogram Using Artificial Intelligence Techniques, (2021) International Review on Modelling and Simulations (IREMOS), 14 (2), pp. 128-136.
https://doi.org/10.15866/iremos.v14i2.19869

Ben Safar, S., A New Approach for Faults Detection and Classification in Overhead Line Systems Using Multiple Methods, (2020) International Review of Electrical Engineering (IREE), 15 (5), pp. 412-420.
https://doi.org/10.15866/iree.v15i5.16800

Akho-Zahieh, M., Abdellatif, N., Effect of Coding and Diversity on the Performance of Wavelet Packets Multicarrier Multicode CDMA System, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (1), pp. 1-15.
https://doi.org/10.15866/irecap.v10i1.18136

A. R. M. Al-shamasneh, and U. H. B. Obaidellah, Artificial intelligence techniques for cancer detection and classification: review study, (2017) European Scientific Journal, vol. 13, no. 3, pp. 342-370.
https://doi.org/10.19044/esj.2017.v13n3p342

J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, A distinctive approach in brain tumor detection and classification using MRI, (2017) Pattern Recognition Letters, vol. 139, pp. 118-127.
https://doi.org/10.1016/j.patrec.2017.10.036

A. R. Mathew, and P. B. Anto, Tumor detection and classification of MRI brain image using wavelet transform and SVM, (2017) 2017 International Conference on Signal Processing and Communication (ICSPC), pp. 75-78.
https://doi.org/10.1109/CSPC.2017.8305810

M. Saii, and Z. Kraitem, Automatic brain tumor detection in MRI using image processing techniques, (2017) Biomedical statistics and informatics, vol. 2, no. 2, pp. 73-76.

Bonfante, F., Maggiore, P., Grimaccia, F., Filippone, E., Dalla Vedova, M., Specific Category Operations Within the U-Space: Expert Systems Embedded Within RPAS as an Original Mean of Mitigation of Operational Safety Risks, (2021) International Review of Aerospace Engineering (IREASE), 14 (3), pp. 121-130.
https://doi.org/10.15866/irease.v14i3.19985

Hastuti, K., Andono, P., Shidik, G., Noersasongko, E., Syarif, A., Gamelan Composer: a Rule-Based Interactive Melody Generator for Gamelan Music, (2020) International Journal on Engineering Applications (IREA), 8 (4), pp. 148-157.
https://doi.org/10.15866/irea.v8i4.19356

Al-Tarawneh, M., Muheilan, M., Al Tarawneh, Z., Hand Movement-Based Diabetes Detection Using Machine Learning Techniques, (2021) International Journal on Engineering Applications (IREA), 9 (4), pp. 234-242.
https://doi.org/10.15866/irea.v9i4.20616

Kaissari, S., El Attaoui, A., Benba, A., Jilbab, A., Bourouhou, A., Kaissari, A., PlanTech: Early Detection of Plant Disease Based on HWSN Using Deep Learning, (2021) International Journal on Engineering Applications (IREA), 9 (3), pp. 162-172.
https://doi.org/10.15866/irea.v9i3.20720

Gorshkova, K., Zueva, V., Kuznetsova, M., Tugashova, L., Optimizing Deep Learning Methods in Neural Network Architectures, (2021) International Review of Automatic Control (IREACO), 14 (2), pp. 93-101.
https://doi.org/10.15866/ireaco.v14i2.20591

S. Do, K. D. Song, and J. W. Chung, Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning, (2020) Korean J Radiol, vol. 21, no. 1, pp. 33-41.
https://doi.org/10.3348/kjr.2019.0312

Triqui, B., Benyettou, A., Diabetes Prediction Using Feature Selection, (2021) International Journal on Engineering Applications (IREA), 9 (2), pp. 94-103.
https://doi.org/10.15866/irea.v9i2.20471

Wang, Z., Al Said, N., Analog Computing and a Hybrid Approach to the Element Base of Artificial Intelligence Applications, (2020) International Review of Automatic Control (IREACO), 13 (5), pp. 206-213.
https://doi.org/10.15866/ireaco.v13i5.19142

A. Ari, and D. Hanbay, Deep learning based brain tumor classification and detection system, (2018) Turkish Journal of Electrical Engineering & Computer Sciences, vol. 26, no. 5, pp. 2275-2286.
https://doi.org/10.3906/elk-1801-8

Oladipo, S., Sun, Y., Wang, Z., Optimization of PID Controller with Metaheuristic Algorithms for DC Motor Drives: Review, (2020) International Review of Electrical Engineering (IREE), 15 (5), pp. 352-381.
https://doi.org/10.15866/iree.v15i5.18688

B. G. Elshaikh, M. E. M. Garelnabi, H. Omer, A. Sulieman, B. Habeeballa, and R. A. Tabeidi, Recognition of brain tumors in MRI images using texture analysis, (2021) Saudi Journal of Biological Sciences, vol. 28, no. 4, pp. 2381-2387.
https://doi.org/10.1016/j.sjbs.2021.01.035

Matar, M., Mohamed, O., Fault Classification on a Power Transmission Line Using Discrete Wavelet Transform and Artificial Neural Networks, (2019) International Review of Electrical Engineering (IREE), 14 (5), pp. 349-357.
https://doi.org/10.15866/iree.v14i5.17017

Gupta, K., Dhanda, N., Kumar, U., A Novel Approach to Brain Tumor Detection Using Texture Based Gabor Filter Followed by Genetic Algorithm, (2021) International Journal on Communications Antenna and Propagation (IRECAP), 11 (4), pp. 233-241.
https://doi.org/10.15866/irecap.v11i4.20766

A. Hamzenejad, S. J. Ghoushchi, and V. Baradaran, Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm, (2021) BioMed Research International, vol. 2021, pp. 5516819.
https://doi.org/10.1155/2021/5516819

F. Özyurt, E. Sert, E. Avci, and E. Dogantekin, Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy, (2019) Measurement, vol. 147, pp. 106830.
https://doi.org/10.1016/j.measurement.2019.07.058

P. M. Shakeel, T. E. E. Tobely, H. Al-Feel, G. Manogaran, and S. Baskar, Neural network based brain tumor detection using wireless infrared imaging sensor, (2019) IEEE Access, vol. 7, pp. 5577-5588.
https://doi.org/10.1109/ACCESS.2018.2883957

A. M. Alqudah, H. Alquraan, I. A. Qasmieh, A. Alqudah, and W. Al-Sharu, Brain Tumor Classification Using Deep Learning Technique--A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes, (2020) International Journal of Advanced Trends in Computer Science and Engineering, pp. 3684-3691.
https://doi.org/10.30534/ijatcse/2019/155862019

M. M. Badža, and M. Č. Barjaktarović, Classification of brain tumors from MRI images using a convolutional neural network, (2020) Applied Sciences, vol. 10, no. 6, pp. 1999.
https://doi.org/10.3390/app10061999

S. Irsheidat, and R. Duwairi, Brain Tumor Detection Using Artificial Convolutional Neural Networks, (2020) 11th International Conference on Information and Communication Systems (ICICS), pp. 197-203.
https://doi.org/10.1109/ICICS49469.2020.239522

R. Mehrotra, M. Ansari, R. Agrawal, and R. Anand, A Transfer Learning approach for AI-based classification of brain tumors, (2020) Machine Learning with Applications, vol. 2, pp. 100003.
https://doi.org/10.1016/j.mlwa.2020.100003

M. Sharif, J. Amin, M. Raza, M. A. Anjum, H. Afzal, and S. A. Shad, Brain tumor detection based on extreme learning, (2020) Neural Computing and Applications, pp. 1-13.
https://doi.org/10.1007/s00521-019-04679-8


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize