Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors

Main Article Content

Umar S. Alqasemi
Sultan A. Almutawa
Shadi M. Obaid

Abstract

Detection and classification of brain tumors in manual or traditional way is an area which could be improved by having such automated detection and clarification system for brain tumors. In this paper, enhanced Computer-Aided Diagnosis CAD software system is introduced for brain tumor detection and classification. Total of 229 brain MRI images was taken as dataset for the purpose of this research; those dataset images include 105 normal brain MRI images, and 124 abnormal brain MRI images. Proposed CAD system is specialized for Meningioma brain tumor detection and classification, and the technique could be generalized and implemented for Glioma, and Pituitary brain tumors as well, and the whole system was implemented using MATLAB software. We started by cropping the region of interest (ROI) of dataset images. Then, feature extraction was implemented using first order statistical features, as well as using of some wavelets filters in combination with the former. T-test is used to exclude features of no statistical significance (p-value < 0.05).  After that, different types of classifiers were used to separate the normal set from the abnormal one. Note that, we used an iterative approach to by changing features with many runs until we got best performance, where, best accuracy results were gotten with SVM-Kernel Function (Linear), KNN-1, KNN-3, and KNN-5 classifiers. Note also that, we used convolutional neural networks (CNN) from Deep Learning toolbox of MATLAB as a control method to compare, where the images were fed directly to the CNN. The results were evaluated using performance assessment techniques which are Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Error Rate, and Area Under the Curve (AUC) of Reciever Operator Characteristic (ROC).  With SVM classifier, the best gotten accuracy results were 91 % with CNN classifier, 82% with SVM classifier, and 77 % with KNN classifier. Furthermore, it was very beneficial to find such feature extraction techniques which gave acceptable accuracy results with three different classifiers; this was the case two times as mentioned the study. All proposed CAD system areas was developed and implemented using MATLAB software.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
Umar S. Alqasemi, Sultan A. Almutawa, and Shadi M. Obaid , Trans., “Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors”, IJEAT, vol. 13, no. 3, pp. 40–48, Feb. 2024, doi: 10.35940/ijeat.C4360.13030224.
Section
Articles

How to Cite

[1]
Umar S. Alqasemi, Sultan A. Almutawa, and Shadi M. Obaid , Trans., “Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors”, IJEAT, vol. 13, no. 3, pp. 40–48, Feb. 2024, doi: 10.35940/ijeat.C4360.13030224.
Share |

References

Guan, Y., Aamir, M., Rahman, Z., Ali, A., Ahmed Abro, W., & Ahmed Dayo, Z. et al. (2021). A framework for efficient brain tumor classification using MRI images. AIMS Press. Retrieved 8 February 2022, from http://www.aimspress.com/article/doi/10.3934/mbe.2021292. (Guan et al., 2021)

S. Musallam, A., S. Sherif, A., & K. Hussein, M. (2022). A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images. Ieeexplore.ieee.org. Retrieved 12 February 2022, from https://ieeexplore.ieee.org/document/9668965. (S. Musallam et al., 2022)

Brain Tumor - Statistics. Cancer.Net. (2022). Retrieved 13 February 2022, from https://www.cancer.net/cancer-types/brain-tumor/statistics. ("Brain Tumor - Statistics", 2022)

Adult Central Nervous System Tumors Treatment (PDQ®)–Health Professional Version. National Cancer Institute. (2022). Retrieved 13 February 2022, from https://www.cancer.gov/types/brain/hp/adult-brain-treatment-pdq#section_1.3. ("Adult Central Nervous System Tumors Treatment (PDQ®)–Health Professional Version", 2022)

Key Statistics for Brain and Spinal Cord Tumors. Cancer.org. (2022). Retrieved 17 February 2022, from https://www.cancer.org/cancer/brain-spinal-cord-tumors-adults/about/key-statistics.html. ("Key Statistics for Brain and Spinal Cord Tumors", 2022)

Meningioma. Hopkinsmedicine.org. (2022). Retrieved 13 February 2022, from https://www.hopkinsmedicine.org/health/conditions-and-diseases/meningioma. ("Meningioma", 2022)

Meningioma - Symptoms and causes. Mayo Clinic. (2019). Retrieved 14 February 2022, from https://www.mayoclinic.org/diseases-conditions/meningioma/symptoms-causes/syc-20355643. ("Meningioma - Symptoms and causes", 2019)

Brain and Spinal Cord Tumor in Adults Causes, Risk Factors, and Prevention. Cancer.org. (2022). Retrieved 14 February 2022, from https://www.cancer.org/cancer/brain-spinal-cord-tumors-adults/causes-risks-prevention.html. ("Brain and Spinal Cord Tumor in Adults Causes, Risk Factors, and Prevention", 2022)

Rezaei, K., Agahi, H. and Mahmoodzadeh, A., 2020. A Weighted Voting Classifiers Ensemble for the Brain Tumors Classification in MR Images. Retrieved 13 February 2020 [online] Available at: https://www.tandfonline.com/doi/full/10.1080/03772063.2020.1780487 (Rezaei, Agahi and Mahmoodzadeh, 2020)

Deepak, S., & Ameer, P. (2020). Automated Categorization of Brain Tumor from MRI Using CNN features and SVM. Retrieved 14 February 2022, from https://link.springer.com/article/10.1007/s12652-020-02568-w. (Deepak & Ameer, 2020)

Faleh Alanazi, M., Umair Ali, M., Javeed Hussain, S., Zafar, A., Mohatram, M., Irfan, M., AlRuwaili, R., Alruwaili, M., H. Ali, N. and Mohammad Albarrak, A., 2022. Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model Retrieved 15 February 2020. (Faleh Alanazi et al., 2022)

M. Sarhan, A., 2020. Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network, Retrieved 13 February 2020 (M. Sarhan, 2020)

AlKubeyyer, A., Ben Ismail, M., Bchir, O., & Alkubeyyer, M. (2020). Automatic detection of the meningioma tumor firmness in MRI images. Retrieved 11 February 2020, from https://content.iospress.com/articles/journal-of-x-ray-science-and-technology/xst200644. (AlKubeyyer et al., 2020)

P.M., A., & S., D. (2019). Brain tumor classification using deep CNN features via transfer learning. Retrieved 14 February 2022, from https://www.sciencedirect.com/science/article/abs/pii/S0010482519302148?via%3Dihub. (P.M. & S., 2019)

M. Sarhan, A. (2020). Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform. Retrieved 10 February 2022, from https://www.scirp.org/journal/paperinformation.aspx?paperid=100953. (M. Sarhan, 2020)

R. Ismael, M., & Abdel-Qader, I. (2018). Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network. Retrieved 10 February 2022, from https://ieeexplore.ieee.org/document/8500308. (R. Ismael & Abdel-Qader, 2018)

Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., & KamalAhuja, C. (2016). A package-SFERCB-“Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors”. Retrieved 15 February 2022, from https://www.sciencedirect.com/science/article/abs/pii/S1568494616302216?via%3Dihub. (Sachdeva et al., 2016)

A. El-Dahshan, E., M. Mohsen, H., Revett, K., & M. Salem, A. (2014). Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Retrieved 10 February 2022, from https://personalpages.manchester.ac.uk/staff/fumie.costen/pastwork/DATA/readingmaterial/braincancerclassification.pdf. (A. El-Dahshan et al., 2014)

Brain Tumor Classification (MRI). Kaggle.com. (2020). Retrieved 2 March 2022, from https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri. ("Brain Tumor Classification (MRI)", 2020)

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>