Abstract
Brain tumor detection and classification is a main concern owing to the global fatalities caused by it. Computer-aided design (CAD) techniques for classification of brain tumor are benefitting radiologists and doctors for error-free detection and correct prognosis. Convolutional neural network (CNN) is the most sought-after framework in deep learning for brain cancer detection due to its robust nature and efficient handling of large datasets. Pre-processing has a pivotal role in brain tumor classification framework architecture. Benchmark datasets acquired for training and testing of the CNN framework have to be pre-processed before being fed to the framework. Pre-processing techniques like data shuffling, resizing, normalization, and augmentation are done to enhance the image quality for its effective analysis. It also increases the reliability of the model by decreasing the learning time. The problem of underfitting and overfitting is also overcome by adhering to pre-processing techniques prior to feeding the dataset to the designed framework in deep neural nets. In this paper, benchmark dataset for brain tumor classification has been pre-processed in two different manners before being fed to the deep CNN model for the classification of brain tumor and results are compared.
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References
Abbasi M, Eslami B, Rezaei Z (2019) Brain tumor classification using deep learning methods, vol 16, no Special Issue. Springer Singapore. https://doi.org/10.5812/iranjradiol.99160
Alsubai S, Khan HU, Alqahtani A, Sha M, Abbas S, Mohammad UG (2022) Ensemble deep learning for brain tumor detection. Front Comput Neurosci 16. https://doi.org/10.3389/fncom.2022.1005617
Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Futur Gener Comput Syst 87:290–297. https://doi.org/10.1016/j.future.2018.04.065
Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS (2021) Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst
Badža MM, Barjaktarović MC (2020) Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 10(6)
Bhardwaj N, Sood M, Gill S (2022) Deep learning framework using CNN for brain tumor classification. In: 2022 5th international conference on multimedia, signal processing and communication technologies (IMPACT), Aligarh, India, pp 1–5. https://doi.org/10.1109/IMPACT55510.2022.10029043
Br35H: Brain tumor detection 2020 | Kaggle. https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection. Accessed 3 Aug 2022
Brain Tumor MRI Dataset | Kaggle. https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset. Accessed 18 Jan 2023
Brain tumor: grades and prognostic factors | Cancer.Net. https://www.cancer.net/cancer-types/brain-tumor/grades-and-prognostic-factors. Accessed 20 July 2022
Çinar A, Yildirim M (2020) Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med Hypotheses 139(March):109684
Çınarer G, Emiroğlu BG, Arslan RS, Yurttakal AH (2020) Brain tumor classification using deep neural network. Adv Sci Technol Eng Syst 5(5):765–769. https://doi.org/10.25046/AJ050593
Dang K, Vo T, Ngo L, Ha H (2022) A Deep learning framework integrating MRI image preprocessing methods for brain tumor segmentation and classification. IBRO Neurosci Reports 13(June):523–532. https://doi.org/10.1016/j.ibneur.2022.10.014
DeAngelis LM (2001) Brain tumors. Med Prog N Engl J Med 114(2):114–123
Deng L, Yu D (2014) Deep learning: methods and applications. Found. Trends® Signal Process 7(3–4):197–387. https://doi.org/10.1561/2000000039
DÃaz-Pernas FJ, MartÃnez-Zarzuela M, González-Ortega S, Antón-RodrÃguez M (2021) A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare 9(2). https://doi.org/10.3390/healthcare9020153
Fan C, Chen M, Wang X, Wang J, Huang B (2021) A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Front Energy Res 9(March):1–17. https://doi.org/10.3389/fenrg.2021.652801
Hashemi M (2019) Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation. J Big Data 6(1). https://doi.org/10.1186/s40537-019-0263-7
Irmak E (2021) Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran J Sci Technol - Trans Electr Eng 45(3):1015–1036
Johnson DR, Guerin JB, Giannini C, Morris JM, Eckel LJ, Kaufmann TJ (2017) 2016 updates to the WHO brain tumor classification system: what the radiologist needs to know. Radiographics 37(7):2164–2180. https://doi.org/10.1148/rg.2017170037
Masoudi S et al (2021) Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research. J Med Imaging 8(01):1–14. https://doi.org/10.1117/1.jmi.8.1.010901
Muhammad K, Khan S, Del Ser J, Albuquerque VHCD (2021) Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Networks Learn Syst 32(2):507–522. https://doi.org/10.1109/TNNLS.2020.2995800
Nikhil B, Image data pre-processing for neural networks. Becoming Human: Artificial Intelligence Magazine. https://becominghuman.ai/image-data-pre-processing-for-neural-networks-498289068258. Accessed 28 Jan 2023
Rahman T, Islam MS (2022) MRI brain tumor classification using deep convolutional neural network. Springer Singapore. https://doi.org/10.1109/ICISET54810.2022.9775817
Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci 2(6):1–20. https://doi.org/10.1007/s42979-021-00815-1
Shafi ASM, Rahman MB, Anwar T, Halder RS, Kays HME (2021) Classification of brain tumors and auto-immune disease using ensemble learning. Inf Med Unlocked 24:100608. https://doi.org/10.1016/j.imu.2021.100608
Shah HA, Saeed F, Yun S, Park JH, Paul A, Kang JM (2022) A robust approach for brain tumor detection in magnetic resonance images using Finetuned EfficientNet. IEEE Access 10:65426–65438
Srinivas C et al (2022) Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images. J Healthc Eng 2022. https://doi.org/10.1155/2022/3264367
Swati ZNK et al (2019) Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph 75:34–46. https://doi.org/10.1016/j.compmedimag.2019.05.001
Tazin T et al (2021) A robust and novel approach for brain tumor classification using convolutional neural network. Comput Intell Neurosci 2021. https://doi.org/10.1155/2021/2392395
ul Haq A, Li JP, Khan S, Alshara MA, Alotaibi RM, Mawuli CB (2022) DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment. Sci Rep 12(1):1–14. https://doi.org/10.1038/s41598-022-19465-1
Vankdothu R, Hameed MA (2022) Brain tumor MRI images identification and classification based on the recurrent convolutional neural network. Meas Sensors 24(August):100412. https://doi.org/10.1016/j.measen.2022.100412
Vishvanathan S, Vidyapeetham AV, Kp S, Vidyapeetham AV (2020) ICICCT 2019—system reliability, quality control, safety, maintenance and management, no August 2019. https://doi.org/10.1007/978-981-13-8461-5
Xie Y et al (2022) Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): review, challenges, and future perspectives. Diagnostics 12(8). https://doi.org/10.3390/diagnostics12081850
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Bhardwaj, N., Sood, M., Gill, S.S. (2024). Data Pre-processing Techniques for Brain Tumor Classification. In: Mehta, G., Wickramasinghe, N., Kakkar, D. (eds) Innovations in VLSI, Signal Processing and Computational Technologies. WREC 2023. Lecture Notes in Electrical Engineering, vol 1095. Springer, Singapore. https://doi.org/10.1007/978-981-99-7077-3_20
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