A Novel Approach to Identify the Brain Tumour Using Convolutional Neural Network
DOI:
https://doi.org/10.4108/eetpht.9.4337Keywords:
CNN, brain tumour, MRI imagesAbstract
INTRODUCTION: Determining the possibility that an individual is affected by a tumour is an intricate process in today's modern technological and biological age, when feats are reaching unprecedented levels with every passing second. Machine learning modalities could dramatically enhance the accuracy of diagnosis.
OBJECTIVES: Our research makes it feasible to detect tumours early, aiding in early diagnosis, and is a necessity for the curative efforts of cancer patients.
METHODS: In our research model Convolutional Neural Network (CNN) was implemented using Jupiter to give an accurate result.
RESULTS: In our proposed model we got 99% accuracy that is higher than the other results.
CONCLUSION: Our research demonstrates the potential of using machine learning techniques to improve the accuracy and efficiency of medical diagnosis.
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Adate A, Arya D, Shaha A, Tripathy BK (2020) Impact of Deep Neural Learning on Artificial Intelligence Research. pp 69–84 DOI: https://doi.org/10.1515/9783110670905-004
Tripathy B, Mohanty R, Parida S (2022) Brain Tumour Detection Using Convolutional Neural Network-XGBoost
Maheshwari K, Shaha A, Arya D, Rajasekaran R, Tripathy BK (2020) 2 Convolutional Neural Networks: A Bottom-Up Approach. In: 2 Convolutional Neural Networks: A Bottom-Up Approach. De Gruyter, pp 21–50 DOI: https://doi.org/10.1515/9783110670905-002
Singhania U, Tripathy BK (2021) Text-Based Image Retrieval Using Deep Learning. In: Encyclopedia of Information Science and Technology, Fifth Edition. IGI Global, pp 87–97 DOI: https://doi.org/10.4018/978-1-7998-3479-3.ch007
Gupta P, Bhachawat S, Dhyani K, Tripathy BK (2022) A Study of Gene Characteristics and Their Applications Using Deep Learning. In: Roy SS, Taguchi Y-H (eds) Handbook of Machine Learning Applications for Genomics. Springer Nature, Singapore, pp 43–64 DOI: https://doi.org/10.1007/978-981-16-9158-4_4
Bhardwaj P, Guhan T, Tripathy BK (2022) Computational Biology in the Lens of CNN. In: Roy SS, Taguchi Y-H (eds) Handbook of Machine Learning Applications for Genomics. Springer Nature, Singapore, pp 65–85 DOI: https://doi.org/10.1007/978-981-16-9158-4_5
Tripathy BK, Parikh S, Ajay P, Magapu C (2022) 10 - Brain MRI segmentation techniques based on CNN and its variants. In: Chaki J (ed) Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Academic Press, pp 161–183 DOI: https://doi.org/10.1016/B978-0-323-91171-9.00001-6
Prabhavathy P, Tripathy BK, Venkatesan M (2022) Analysis of Diabetic Retinopathy Detection Techniques Using CNN Models. In: Mishra S, Tripathy HK, Mallick P, Shaalan K (eds) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Springer Nature, Singapore, pp 87–102 DOI: https://doi.org/10.1007/978-981-19-1076-0_6
Sihare P, Ullah Khan A, Bardhan P, Tripathy BK (2022) COVID-19 Detection Using Deep Learning: A Comparative Study of Segmentation Algorithms. In: Das AK, Nayak J, Naik B, Vimal S, Pelusi D (eds) Computational Intelligence in Pattern Recognition. Springer Nature, Singapore, pp 1–10 DOI: https://doi.org/10.1007/978-981-19-3089-8_1
Sajid S, Hussain S, Sarwar A (2019) Brain Tumor Detection and Segmentation in MR Images Using Deep Learning. Arab J Sci Eng 44:9249–9261. https://doi.org/10.1007/s13369-019-03967-8 DOI: https://doi.org/10.1007/s13369-019-03967-8
Özyurt F, Sert E, Avci E, Dogantekin E (2019) Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy. Measurement 147:106830. https://doi.org/10.1016/j.measurement.2019.07.058 DOI: https://doi.org/10.1016/j.measurement.2019.07.058
Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM (2018) Classification using deep learning neural networks for brain tumors. Future Comput Inform J 3:68–71. https://doi.org/10.1016/j.fcij.2017.12.001 DOI: https://doi.org/10.1016/j.fcij.2017.12.001
Bauer S, May C, Dionysiou D, Stamatakos G, Büchler P, Reyes M (2012) Multiscale modeling for image analysis of brain tumor studies. IEEE Trans Biomed Eng 59:25–29. https://doi.org/10.1109/TBME.2011.2163406 DOI: https://doi.org/10.1109/TBME.2011.2163406
Islam A, Reza SMS, Iftekharuddin KM (2013) Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors. IEEE Trans Biomed Eng 60:3204–3215. https://doi.org/10.1109/TBME.2013.2271383 DOI: https://doi.org/10.1109/TBME.2013.2271383
Seetha J, Raja SS (2018) Brain Tumor Classification Using Convolutional Neural Networks. Biomed Pharmacol J 11:1457–1461 DOI: https://doi.org/10.13005/bpj/1511
Brindha PG, Kavinraj M, Manivasakam P, Prasanth P (2021) Brain tumor detection from MRI images using deep learning techniques. IOP Conf Ser Mater Sci Eng 1055:012115. https://doi.org/10.1088/1757-899X/1055/1/012115 DOI: https://doi.org/10.1088/1757-899X/1055/1/012115
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Copyright (c) 2023 Suraj Khari, Deepa Gupta, Alka Chaudhary, Ruchika Bhatla
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