AUTOMATIC IDENTIFICATION OF RING ENHANCING LESION PATTERN IN CASES OF BRAIN INFECTION AND METASTASIS BRAIN TUMOR BASED ON INVARIANT MOMENT FEATURES CLASSIFICATION
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Published:
Nov 30, 2019
Keywords:
CT scan identification invariant moment features backpropagation neural network
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Abstract
Brain infection and metastasis brain tumor in CT Scan examination have similar ring-enhancing lesion patterns. This research aims to develop a program that aids the radiologists to identify these brain disorders. The radiologists often have difficulties and mostly subjective when distinguishing the ring-enhancing lesion whether brain infection or metastatic brain tumor, especially in patients with no previous history of the disease. With these limitations, this research produces a Computer Aided Diagnose (CAD) system in order to assist the radiologists in brain disorders observing from the images of CT scan brain scanning. The CAD system is supported by features extraction method which is generated using the combinations of Hu's invariant moment features. The decision maker uses the backpropagation neural network method to classify the brain disorders based on their invariant moment features that could help the radiologists when identifying the brain abnormalities. The results of brain abnormalities identification including normal, brain infection, and metastasis brain tumor yielded performance with 88.9% accuracy, 86% sensitivity, and 100% specificity.
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Rulaningtyas, R., Muqmiroh, L., Ferdian, L., Alfi, A., & Iba, I. (2019). AUTOMATIC IDENTIFICATION OF RING ENHANCING LESION PATTERN IN CASES OF BRAIN INFECTION AND METASTASIS BRAIN TUMOR BASED ON INVARIANT MOMENT FEATURES CLASSIFICATION. Malaysian Journal of Science, 38(Sp3), 42–52. https://doi.org/10.22452/mjs.sp2019no3.5
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UMInd2018 (Published)
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Huang, Z., & Leng, J. (2010). Analysis of Hu’s Moment Invariants on Image Scaling and Rotation. 2nd International Conference on Computer Engineering and Technology (ICCET), Volume 7, 476–480. https://doi.org/10.1109/ICCET.2010.5485542
Rulaningtyas, R, Suksmono, B, Mengko, T, L, R, Putri, S. (2011). Automatic Classification of Tuberculosis Bacteria Using Neural Network. IEEE Xplore, page 1-4.
Sun, W., Xu, Y. (2016). Using a Back Propagation Neural Network Based On Improved Particle Swarm Optimization to Study the Influential Factors of Carbon Dioxide Emissions in Hebei Province, China. Journal of Cleaner Production 112(2016), 1282-1291. Elsevier. Zhang, Y., Yang, J., Wang, S., Dong, Z., & Phillips, P. (2016). Pathological Brain Detection in MRI Scanning via Hu Moment Invariants and Machine Learning. Journal of Experimental & Theoritical Artificial Intelligence, 29(2), 299-312. doi:10.1080/0952813x.2015.1132274.
Zhang, Y., Wang, S., Sun, P., Phillips, P. (2015). Pathological Brain Detection Based on Wavelet Entropy and Hu moment invariants. Bio-Medical Materials and Engineering. Vol.26, Page S1283-S1290. IOP Press.
Zunic, J., Hirota, K., Rosin, P.L. (2010). A Hu moment invariants as A Shape Circularity Measure. Pattern Recognition. Volume 43, Issue 1, Pages 47-57. Elsevier.