Diagnosing Brain Tumors: An Artificial Intelligence Modeling Approach

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Published Apr 15, 2024
Danilo Maurmo Tommaso Ruga Ester Zumpano Eugenio Vocaturo

Abstract

Brain tumour diagnosis poses significant challenges due to the complexity and heterogeneity of tumour characteristics. This study introduces an innovative deep-learning approach that leverages the complementary strengths of weighted magnetic resonance imaging and advanced deep-learning techniques to enhance the accuracy and reliability of brain tumour diagnosis. Historically, diverse methodologies have been employed, often centring on categorizing imaging modalities into binary distinctions, either cancerous versus non-cancerous images or discerning between benign and malignant tumours. In contrast, this study aims to classify multi-class malignant tumours into their specific categories with optimal precision. The proposed research centres on the differentiation of meningioma, glioma, and pituitary adenoma brain tumours, representing the three primary types of malignant brain tumours, along with non-tumorous MRI scans. This study employs a Convolutional Neural Network (CNN) and a Variational Autoencoder for the detection of brain tumours in Magnetic Resonance Imaging (MRI) images. Hyperparameter tuning for the CNN is achieved through a Grid search. The highest-performing model exhibits an accuracy of 95.25%, the other model reaches a peak accuracy of almost 100% in diagnosing cases without tumours.

How to Cite

Maurmo, D., Ruga, T., Zumpano, E., & Vocaturo, E. (2024). Diagnosing Brain Tumors: An Artificial Intelligence Modeling Approach. SPAST Reports, 1(4). Retrieved from https://spast.org/ojspath/article/view/4990
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Article Details

Keywords

Brain Tumors; Early Detection; Neural network; Artificial Intelligence

References
1. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34(10), 1993–2024 (2014)
2. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Medical image analysis 35, 18–31 (2017)
3. Buckner, J.C., Brown, P.D., O’Neill, B.P., Meyer, F.B., Wetmore, C.J., Uhm, J.H.: Central nervous system tumors. In: Mayo Clinic Proceedings. vol. 82, pp. 1271– 1286. Elsevier (2007)
4. Budati, A.K., Katta, R.B.: An automated brain tumor detection and classification from mri images using machine learning technique s with iot. Environment,
Development and Sustainability 24(9), 10570–10584 (2022)
5. Jena, B., Nayak, G.K., Saxena, S.: An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature. Machine Vision and Applications 33(1), 6 (2022)
6. Badˇza, M.M., Barjaktarovi´c, M.C.: Classification of brain tumors from mri imagesˇ using a convolutional neural network. Applied Sciences 10(6), 1999 (2020)
7. Rinesh, S., Maheswari, K., Arthi, B., Sherubha, P., Vijay, A., Sridhar, S., Rajendran, T., Waji, Y.A., et al.: Investigations on brain tumor classification using hybrid machine learning algorithms. Journal of Healthcare Engineering 2022 (2022)
8. Vidyarthi, A., Agarwal, R., Gupta, D., Sharma, R., Draheim, D., Tiwari, P.: Machine learning assisted methodology for multiclass classification of malignant brain tumors. IEEE Access 10, 50624–50640 (2022)
9. Kang, J., Ullah, Z., Gwak, J.: Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 21(6), 2222 (2021)
10. Saeedi, S., Rezayi, S., Keshavarz, H., R. Niakan Kalhori, S.: Mri-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Medical Informatics and Decision Making 23(1), 16 (2023)
11. Khan, M.A., Ashraf, I., Alhaisoni, M., Damaˇseviˇcius, R., Scherer, R., Rehman, A., Bukhari, S.A.C.: Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics 10(8), 565 (2020)
12. Nickparvar, M.: Brain tumor mri dataset, https://www.kaggle.com/datasets/ masoudnickparvar/brain-tumor-mri-dataset?select=Training
13. Cheng, J.: brain tumor dataset (4 2017), https://figshare.com/articles/dataset/ brain tumor dataset/1512427
14. Bhuvaji, S., Kadam, A., Bhumkar, P., Dedge, S., Kanchan, S.: Brain tumor classification (mri) (2020), https://www.kaggle.com/dsv/1183165
15. Hamada, A.: Br35h :: Brain tumor detection 2020, https://www.kaggle.com/ datasets/ahmedhamada0/brain-tumor-detection?select=no
16. Chollet, F.: Deep learning with Python. Simon and Schuster (2021)
17. Aggarwal, C.C., et al.: Neural networks and deep learning. Springer 10(978), 3 (2018)
18. Caroprese, L., Ruga, T., Vocaturo, E., Zumpano, E.: Revealing brain tumor with federated learning. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 3868–3873. IEEE (2023)
19. Varuna Shree, N., Kumar, T.: Identification and classification of brain tumor mri images with feature extraction using dwt and probabilistic neural network. Brain informatics 5(1), 23–30 (2018)
20. Caroprese, L., Vocaturo, E., Zumpano, E.: Argumentation approaches for explanaible ai in medical informatics. Intelligent Systems with Applications 16,
200109 (2022)
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