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Effective classification of medical images using image segmentation and machine learning | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 18، دوره 14، شماره 3، خرداد 2023، صفحه 213-221 اصل مقاله (643.68 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.7200 | ||
| نویسندگان | ||
| Shahab Kareem* 1، 2؛ Farah Sami khoshabaa2؛ Havall Muhssin Mohammad3 | ||
| 1Department of IT, College of Engineering and Computer Science, Lebanese French University, Erbil, Iraq | ||
| 2Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Kirkuk Road, Erbil, Iraq | ||
| 3Department of Computer Information System, Mergasor Technical Institute, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq | ||
| تاریخ دریافت: 26 مرداد 1401، تاریخ بازنگری: 22 شهریور 1401، تاریخ پذیرش: 18 آبان 1401 | ||
| چکیده | ||
| Because of an increase in the frequency of encephalon tumors in each age group, the mortality rate has grown in recent years. In medical imaging, tumors are hard to see because of their complicated structure and noise, which makes it hard and time-consuming for specialists to find them. It is very important to find and pinpoint the tumor's location at an early stage, so this is very important. Medical scans can be used to look for and predict cancerous spots at different levels. These scans can be combined with segmentation and relegation methods to help doctors make an early diagnosis, which can save a lot of time. Physical tumor identification has become a challenging and time-consuming process for medical practitioners due to the intricate structure of tumors and the involution of noise in magnetic resonance (MR) imaging data. As a result, detecting and pinpointing the site of the tumour at an early stage is critical. Medical scans can be used in conjunction with segmentation and relegation procedures to deliver an accurate diagnosis at an early stage in cancer tumour locations at various levels. This research offers a system based on machine learning for segmenting and classifying MRI images for brain tumor identification. The K* classifier, Additive Regression, Bagging, Input Mapped Classifier, and Decision Table algorithms are used in this framework for image preprocessing, image segmentation, feature extraction, and classification. | ||
| کلیدواژهها | ||
| Brain Tumor؛ MRI Images؛ Machine Learning؛ Image Segmentation؛ Feature Extraction | ||
| مراجع | ||
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[1] S.M.R.K. Al-Jumur, S.W. Kareem, and R.Z. Yousif, Predicting temperature of Erbil City applying deep learning and neural network, Indones. J. Electric. Engin. Comput. Sci. 22 (2021), no. 2, 944–952.
[2] J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, Z. Wang, and Q. Feng, Enhanced performance of brain tumor classification via tumor region augmentation and partition, PloS one 10 (2015), no. 10, e0140381.
[3] Harvard, The Whole Brain Atlas.
[4] R.S. Hawezi, F.S. Khoshaba, and S.W. Kareem, A comparison of automated classification techniques for image processing in video internet of things, Comput. Electric. Engin. 101 (2022), 108074.
[5] S.H. Ismael, S.W. Kareem, and F.H. Almukhtar, Medical image classification using different machine learning algorithms, AL-Rafidain J. Comput. Sci. Math. 14 (2020), no. 1, 135–147.
[6] A. I¸sın, C. Direko˘glu, and M. S¸ah, Review of MRI-based brain tumor image segmentation using deep learning methods, Procedia Comput. Sci. 102 (2016), 317–324.
[7] S.-L. Jui, S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A.E. Hassanien, and K. Xiao, Brain MRI tumor segmentation with 3D intracranial structure deformation features, IEEE Intell. Syst. 31 (2015), no. 2, 66–76.
[8] S. Kareem and M.C. Okur, Evaluation of Bayesian network structure learning, vol. 201, 2017.
[9] , Bayesian network structure learning using hybrid bee optimization and greedy search, Adana, Turkey: C¸ ukurova University (2018).
[10] S.W. Kareem, A nature-inspired metaheuristic optimization algorithm based on crocodiles hunting search (CHS), Int. J. Swarm Intell. Res. 13 (2022), no. 1, 1–23.
[11] S.W. Kareem and M.C. Okur, Pigeon inspired optimization of bayesian network structure learning and a comparative evaluation, J. Cognit. Sci. 20 (2019), no. 4, 535–552.
[12] , Falcon optimization algorithm for Bayesian networks structure learning, Comput. Sci. 22 (2021), no. 4.
[13] S.W. Kareem, R.Z. Yousif, and S.M.J. Abdalwahid, An approach for enhancing data confidentiality in hadoop, Indones. J. Electric. Engin. Comput. Sci. 20 (2020), no. 3, 1547–1555.
[14] J. Kim, C. Lenglet, Y. Duchin, G. Sapiro, and N. Harel, Semiautomatic segmentation of brain subcortical structures from high-field MRI, IEEE J. Biomed. Health Inf. 18 (2013), no. 5, 1678–1695.
[15] G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J. Awm Van Der Laak, B. Van Ginneken, and C.I. S´anchez, A survey on deep learning in medical image analysis, Med. Image Anal. 42 (2017), 60–88.
[16] B.H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, and R. Wiest, The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imag. 34 (2014), no. 10, 1993–2024.
[17] G. Mohan and M.M. Subashini, MRI based medical image analysis: Survey on brain tumor grade classification, Biomed. Signal Process. Control 39 (2018), 139–161.
[18] M. Osadebey, M. Pedersen, D. Arnold, and K. Wendel-Mitoraj, No-reference quality measure in brain MRI images using binary operations, texture and set analysis, IET Image Process. 11 (2017), no. 9, 672–684.
[19] S. Pereira, A. Pinto, V. Alves, and C.A. Silva, Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Med. Imag. 35 (2016), no. 5, 1240–1251.
[20] S. Poornachandra and C. Naveena, Pre-processing of mr images for efficient quantitative image analysis using deep learning techniques, IEEE, 2017, pp. 191–195.
[21] J. Sachdeva, V. Kumar, I. Gupta, N. Khandelwal, and C.K. Ahuja, A package-SFERCB-“Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors”, Appl. Soft Comput. 47 (2016), 151–167.
[22] R.V. Suryavamsi, L.S.T. Reddy, S. Saladi, and Y. Karuna, Comparative analysis of various enhancement methods for astrocytoma MRI images, IEEE, 2018, pp. 0812–0816.
[23] H.-Y. Tsai, H. Zhang, C.-L. Hung, and G. Min, GPU-accelerated features extraction from magnetic resonance images, IEEE Access 5 (2017), 22634–22646.
[24] S. Widyarto, S.R.B. Kassim, and W.K. Sari, 2D-sigmoid enhancement prior to segment MRI Glioma tumour: Pre image-processing, IEEE, 2017, pp. 1–5.
[25] R.Z. Yousif, S.W. Kareem, and S.M. Abdalwahid, Enhancing approach for information security in hadoop, Polytech. J. 10 (2020), no. 1, 81–87.
[26] J. Zhang, M. Liu, L. An, Y. Gao, and D. Shen, Alzheimer’s disease diagnosis using landmark-based features from longitudinal structural MR images, IEEE J. Biomed. Health Inf. 21 (2017), no. 6, 1607–1616. | ||
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