Brain Tumor Diagnosis Using MR Image Processing

  • Sura Yarub Kamil Computer Science Department, College of Science-Al-Nahrain University, Baghdad, Iraq
  • Mohammed Sahib Mahdi Altaei Computer Science Department, College of Science-Al-Nahrain University, Baghdad, Iraq
Keywords: MRI images, Brain tumors, Detection, Classification, SIFT

Abstract

Magnetic Resonance Imaging (MRI) images of brain are a process of high importance to diagnosing brain tumors. Brain tumor is an abnormal growth of cells in the brain. These tumors may be benign or malignant.  The use of computer technologies became widely used to store and manage medical images for supporting medical decision and improve the accuracy of radiologists with a reduction of time in the interpretation of images. The present study aimed to establish a Computer-Aided Detection and Diagnosis (CADD) system dealing with medical MRI for classifying input digital image into normal or abnormal tumors, also the type of abnormal case is diagnosed into benign or malignant tumor.  The proposed method is considered to be contained four stages within, they are: Pre-processing stage, image segmentation for determining the Region of Interest (ROI), Feature extraction based on Scale Invariant Feature Transform (SIFT) descriptor, and then classification. The results of classification are evaluated by cross validation technique, in which the dataset are divided into training set and testing set. To evaluate the achieved results, the classification is carried out using two levels for each case: logistics technique is used to check the results of normal case, and random forest to check the results of abnormal cases. Results of normal classification showed that the accuracy of applying Logistic Regression was 93.3%, whereas the classification score of abnormal cases was 99.9% for Random Forest, which ensure the success of the classification system and correct path of the computations.

References

[1] Tahir; M., N.; "Classification and characterization of brain tumor MRI by using gray scaled segmentation and DNN, “International Joint Conference on Computer Vision Theory and Applications, 2018.
[2] Gopal, S. T.; "A Review on a Deep Learning Perspective in Brain Cancer Classification." International Joint Conference on Computer Vision Theory and Applications Cancers 11(1), 111, 2019.
[3] Abdulla, A. S.; Bushra, Q. A. A.; Mohammed, S. M.; "Classification of Al-Hammar Marshes Satellite Images in Iraq using Artificial Neural Network based on Coding Representation." Ind. J. Ecol. 45(4), 728–736, 2018.
[4] Dandu, J. R.; "Brain and pancreatic tumor segmentation using SRM and BPNN classification." Health and Technology, 1–9, 2019.
[5] Cheung, W.; Ghassan, H.; "SIFT in 3Dimensional Scale Invariant Feature Transform." IEEE Trans. Im. Proc. 18(9), 2021, 2012.
[6] Mohammed, S. M. A.; Saif, M. A.; "Satellite Image Classification using Multi Features Based Descriptors", Int. Res. J. Adv. Eng. Sci., 2018.
[7] Sachdeva, J.; "A package-SFERCB-“Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors”." Applied soft computing 47, 151–167, 2016.
[8] Kamavisdar, P.; Sonam, S.; Sonu, A.; "A survey on image classification approaches and techniques." Int. J. Adv. Res. Comp. Comm. Eng. 2(1), 1005–1009, 2013.
[9] O'Tool, A. J.; "Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data." J. Cog. Neuro. 19(11), 1735–1752, 2007.
[10] Amulya, C.; Prathibha, G.; "MRI Brain Tumor Classification Using SURF and SIFT Features." Int. J. Modern Tren. Sci. Technol. 2(7) 123–127, 2016.
[11] Alfonse, M.; Abdel, B. M. S.; "An automatic classification of brain tumors through MRI using support vector machine." Egy. Comp. Sci. J. 40(3), 2016.
[12] Gurusamy, R.; Vijayan, S.; "A machine learning approach for MRI brain tumor classification." Computers, Materials and Continua 53(2), 91–108, 2017.
[13] Bahadure, N. B.; Arun, K. R.; Har, P. T.; "Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM." Int. J. Biomed. Im., 2017.
[14] Bahadure, N. B.; Arun, K. R.; Har, P. T. ; "Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm.", J. Digit. Imag. 31(4), 477–489, 2018.
[15] Dandu, J. R.; "Brain and pancreatic tumor segmentation using SRM and BPNN classification.", Health and Technology 1, 2019.
[16] Polepaka, S.; Ch, S. R.; Chandra, M.; "A Brain Tumor: Localization Using Bounding Box and Classification Using SVM.", Innov. Electro. Commun. Eng., 61–70, 2019.
Published
2020-06-04
How to Cite
Yarub Kamil, S., & Sahib Mahdi Altaei, M. (2020). Brain Tumor Diagnosis Using MR Image Processing. Al-Nahrain Journal of Science, 23(2), 67-74. Retrieved from https://anjs.edu.iq/index.php/anjs/article/view/2280
Section
Articles