The use of artificial intelligence-supported communication technologies in neurological fields: A case study on brain tumor detection

Abstract

Objective: The global health system is being shaped by multidisciplinary studies on the diagnosis of diseases and the provision of effective treatment services. Information and communication technologies have been developing laboratory and imaging studies through artificial intelligence-supported systems for the last twenty years. Studies with high accuracy levels in the diagnosis and treatment protocols of diseases make important contributions to making healthy decisions. Artificial intelligence applications have been actively used in the treatment processes of neurological cancer cases in the field of health, as in many fields in recent years. Among these applications, the machine learning model has started to be preferred in the detection of brain tumors because it can provide remarkable results. The main purpose of the study is to provide a supportive analysis for the organization of early diagnosis and rapid treatment in areas such as intracranial pressure, tumor treatment and radiotherapy of patients during intensive care processes. Materials and Methods: In this study, the method developed by doctors with machine learning Kaggle and developers of samples in the network through an example of an application that was developed through machine learning on brain tumors, brain tumor detection carried on with the validation of the data sets includes four classifications. Results: The study consists of two different study systems, namely practice and test. Sectional images from 2865 brain magnetic resonance imaging (MRI )and computed tomography (CT) samples were examined as training in the first stage of the application using the convolutional neural network (CNN) model, and the detected tumors were classified. In this context, MRI results were obtained on 2865 samples with 2470 units and 86.23% with tumors, and 395 units and 13.76% no tumors. Conclusion: In the study, samples with tumors were detected in a 3-month period for brain tumor detection with artificial intelligence and classified typologically. Accordingly, the reliability of the application was proven by providing 98.55% verification on 2865 samples, 3 different tumor types and no tumor data.

Keywords

• Tumor detection
• MRI
• Artificial intelligence
• Kaggle
• Case study

References

1. Bhattacharyya D, Kim TH. Brain tumor detection using MRI image analysis. In: Kim Th, Adeli H, Robles R.J, Balitanas M. eds. Ubiquitous computing and multimedia applications. UCMA 2011, Part II, CCIS 151, Springer-Verlag Berlin Heidelberg, 2011; 307-14.
2. Vijayakumar T. Classification of brain cancer type using machine learning, J Artif Intell Caps Netw 2019; 1: 105-13. doi: 10.36548/jaicn.2019.2.006.
3. National Brain Tumor Society. The Essential guide to brain tumors national brain tumor society. https://biak.us/wpcontent/ uploads/2016/06/Essential-Guide-for-Brain-Tumors. pdf Accessed 11.02.2023
4. Mahapatra D, Bozorgtabar B, Garnavi R. Image superresolution using progressive generative adversarial networks for medical image analysis. Comput Med Imaging Graph 2019; 71:30-9. doi: 10.1016/j.compmedimag.2018.10.005.
5. Roy S, Nag S, Maitra IK, Bandyopadhyay SKA Review on automated brain tumor detection and segmentation from MRI of brain, Int J Adv Res Comput Sci Soft Eng 2013; 1:1-41. doi: 10.48550/arXiv.1312.6150.
6. Shankar K, Elhoseny M, Lakshmanaprabu SK, et al. Optimal feature level fusion based ANFIS classifier for brain MRI image classification. Concurr Comput 2020;32: e4887. doi: 10.1002/cpe.4887.
7. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016; 278: 563-77.
8. Clarke LP, Velthuizen RP, Camacho MA, et al. MRI segmentation: Methods and applications. Magn Reson Imaging 1995; 13:343-68.

9. Vankdothu R, Hameed MA. Brain tumor MRI images identification and classification based on the recurrent convolutional neural network. Measurement Sensors 2022; 24:1-11. doi: 10.1016/j.measen.2022.100412.
10. Damodharan S, Raghavan D. Combining tissue segmentation and neural network for brain tumor detection, Int Arab J Inf Technol 2015; 12:42-52.
11. Ratan R, Sharma S, Sharma SK. Multiparameter segmentation quantization of brain tumor from MRI images. ISEE-IJST J 2009; 2:11-15. doi: 10.17485/ijst/2009/v2i2/29385.
12. Tzika A, Astrakas L, Zarifi M. Pediatric brain tumors: Magnetic resonance spectroscopic imaging, diagnostic techniques and surgical management of brain tumors, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA, 2011:205-26. doi: 10.5772/22273.
13. Packer RJ, Friedman HS, Kun LE, Fuller GN. Tumors of the brain stem cerebellum and fourth ventricle. 2002;171-92 https://www.socneuroonc.org/UploadedFiles/Levin/Levin_ ch06_p171-192.pdf. Accessed 12.02.2023.
14. Gopal NN, Karnan M. Diagnose brain tumor through MRI using image processing clustering algorithms such as fuzzy c means along with intelligent optimization techniques, IEEE Int Conf Comput Intell Comput Res 2010; 1-4. doi: 10.1109/ ICCIC.2010.570.5890.
15. El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM. Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm, Expert Syst Appl J 2014; 41:5526-45. doi: 10.1016/j.eswa.2014.01.021.
16. Mohsen H, El-Dahshan EA, El-Horbaty EM, Salem AM. Classification using deep learning neural networks for brain tumors, Future Computing Inform J 2018; 3:68-71. doi: 10.1016/j.fcij.2017.12.001.
17. Yang Y, Yan LF, Zhang X, et al. Glioma grading on conventional mr images: a deep learning study with transfer learning, Front Cell Neurosci 2018;12:1-10. doi: 10.3389/fnins.2018.00804.
18. Dahab DA, Ghoniemy SSA, Selim GM. Automated brain tumor detection and identification using image processing and probabilistic neural network techniques, Int J Vis Commun Image Process 2012; 1:1-8.
19. Zaw HT, Maneerat N, Win KY. Brain tumor detection based on naive bayes classification, 2019 5th International conference on engineering, applied sciences and technology (ICEAST), 2019;1-4, doi: 10.1109/ICEAST.2019.880.2562. Nie D, Li Y, Wang Y, et al. Deep learning-based brain tumor diagnosis and prognosis prediction using multimodal MR images. Sci Rep 2017; 7:16936.
20. Özyurt F, Sert E, Avci E, Dogantekin E. Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy. Measurement 2019; 147:106830. doi: 10.1016/j.measurement.2019.07.058.
21. Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multiscale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med Image Anal 2017; 36:61-78. doi: 10.1016/j. media.2016.10.004.
22. Balasooriya NM, Nawarathna RD. A sophisticated convolutional neural network model for brain tumor classification. IEEE International conference on industrial and information systems (ICIIS), 2017;1-5. doi: 10.1109/ICIINFS.2017.830.0364.
23. Menze B, Jakab A, Bauer S, et al. The Multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2014; 34:1993-2024. doi: 10.1109/TMI.2014.237.7694.
24. Parmar C, Vora H, Patel S. Brain tumor detection and classification using convolutional neural network. Int J Adv Res Compute Sci Soft Eng 2018; 8:184-89.
25. Mao H, Yao S, Tang T, Li B, Yao J, Wang Y. Towards realtime object detection on embedded systems, IEEE Trans Emerg Topics Comput 2018; 6:417-31. doi: 10.1109/TETC.2016.259.3643.
26. Li Y, Nie D, Chen H, et al. A deep learning model for improved brain tumor segmentation in multi-sequence MR images. Neurocomputing 2017; 260:172-82.
27. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z. Ahmed S, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph 2019; 75:34-46. doi: 10.1016/j.compmedimag.2019.05.001.
28. Özyurt F, Sert E, Avci E. An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine, Med Hypotheses 2020; 134:109433 doi: 10.1016/j. mehy.2019.109433.
29. Kurup RV, Sowmya V, Soman KP. Effect of data pre-processing on brain tumor classification using capsulenet. Springer Singapore, 2020, Singapore.
30. Woźniak M, Siłka J, Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput Applic 2023; 35:14611-626. doi: 10.1007/ s00521.021.05841-x.
31. Seetha J, Selvakumar RS. Brain tumor classification using convolutional neural networks. Biomed Pharmacol J 2018; 11:1457-61. doi: 10.13005/bpj/1511.
32. Hossain T, Shishir FS, Ashraf M, Al Nasim MDA, Shah FM. Brain tumor detection using convolutional neural network, 1st International conference on advances in science, engineering and robotics technology (ICASERT) 2019; 1:1-6. doi: 10.1109/ ICASERT.2019.893.4561.
33. Kachwalla M, Shinde MP, Katare R, Agrawal A, Wadhai VM. Jadhav MS. Classification of brain MRI images for cancer detection using deep learning. Int J Adv Res Comput Commun Eng 2017; 3:635 37. doi: 10.17148/ IJARCCE.2018.7454.
34. Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ. A Hybrid cnn-svm threshold segmentation approach for tumor detection and classification of MRI brain images, IRBM 2022; 43:290-99. doi: 10.1016/j.irbm.2021.06.003.