Analysis of Diagnosis for Malignant and Benign Brain Tumor MRI Images using CNN and DWT Technique

Authors

  • Prerana A. Wankhede Research Scholar, G.H.Raisoni University, Anjangaon Bari Road, Amravati, India
  • Swati R. Dixit Faculty, Department of E & TC Engineering, G.H.Raisoni University, Anjangaon Bari Road, Amravati, India

Keywords:

Artificial Intelligence, Discrete Wavelet Transform (DWT), MRI (Magnetic Resonance Imaging), image filtering, image segmentation

Abstract

The article describes the use of image processing in the search for the brain tumor. Tumors are a worldwide health crisis that may manifest in any part of the body. If a brain tumor is not diagnosed and treated early on, it may significantly reduce a patient's lifespan. Malignant and benign tumors of varied stages were discovered. This illness is now affecting a sizable population. Each year, more and more people are screened every day in an effort to discover diseases early. Manual screening is not only a time-consuming process, but it also raises the possibility of making mistakes. Some people could become even more distracted. Therefore, it is more ideal to use an AI-based system for the screening process than a human one. Specialists employ MRI (Magnetic Resonance Imaging) scan images to identify brain cancers; however, these images include noise that must be minimized in the first phases of processing if accurate findings are to be achieved. In this research, we provide a refined Discrete Wavelet Transform (DWT) filtering technique along the used of artificial Intelligence for this purpose. Image filtering and image segmentation are the system's two main components. Brain tumor MRI images from the Kaggle dataset will be used as test data for the filter.

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References

G. N, V. Pushpalatha, R. C, S. L and S. S, "Brain Tumor Detection and Classification Using Deep Learning," 2023 Winter Summit on Smart Computing and Networks (WiSSCoN), Chennai, India, 2023, pp. 1-6, doi: 10.1109/WiSSCoN56857.2023.10133851.

S. H. Eshan, R. R. Hasan, A. Al Mamun Sarker, S. Zabin, R. T. H. Tusher and M. A. Rahman, "Brain tumor detection by Kapton Polyimide based on-body patch antenna in K band," 2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 2023, pp. 165-169, doi: 10.1109/ICREST57604.2023.10070083.

H. Hu, X. Li, W. Yao and Z. Yao, "Brain Tumor Diagnose Applying CNN through MRI," 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Hangzhou, China, 2021, pp. 430-434, doi: 10.1109/ICAICE54393.2021.00090.

D. Rastogi, P. Johri and V. Tiwari, "Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet Deep Learning Architecture," 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), MORADABAD, India, 2021, pp. 723-732, doi: 10.1109/SMART52563.2021.9676317.

S. Ahuja, B. K. Panigrahi, T. Gandhi and U. Gautam, "Deep learning-based computer-aided diagnosis tool for brain tumor classification," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 854-859, doi: 10.1109/Confluence51648.2021.9377171.

M. Acharya et al., "MRI-based Diagnosis of Brain Tumors Using a Deep Neural Network Framework," 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), Sydney, Australia, 2020, pp. 1-5, doi: 10.1109/CITISIA50690.2020.9371831.

V. Sravan, K. Swaraja, K. Meenakshi, P. Kora and M. Samson, "Magnetic Resonance Images Based Brain Tumor Segmentation- A critical survey," 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), Tirunelveli, India, 2020, pp. 1063-1068, doi: 10.1109/ICOEI48184.2020.9143045.

S. Grampurohit, V. Shalavadi, V. R. Dhotargavi, M. Kudari and S. Jolad, "Brain Tumor Detection Using Deep Learning Models," 2020 IEEE India Council International Subsections Conference (INDISCON), Visakhapatnam, India, 2020, pp. 129-134, doi: 10.1109/INDISCON50162.2020.00037.’

Sonia kuruvilla, J.Anitha,“Comparision of registered multimodal medical image fusion techniques”, International Conference on Electronics and Communication systems,2014.

Ramandeep kaur, Sukhpreet kaur,“An approach for image fusion using PCA and Genetic Algorithm”, International Journal of computer applications (0975-8887), volume 145, no.6, July 2016.

Y. D. Zhang and L. Wu, “An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine,” Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.

A. Aslam, E. Khan and M. M. S. Beg, “Improved Edge Detection Algorithm for Brain Tumor Segmentation,” Procedia Computer Science, 58I: 430-437, 2015.

N. Nabizadeh and M. Kubat, “Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features,” Computers & Electrical Engineering, 45:286- 301, 2015.

P. Shanthakumar and P. Ganeshkumar, “Computer aided brain tumor detection system using watershed segmentation techniques,” International Journal of Imaging Systems and Technology, Vol. 25(4): pp. 297- 301, 2015.

E. Dandil et al., “Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images,” Advances in Intelligent Systems and Computing, vol 311, Springer, 2015.

S. R. Telrandhe, A. Pimpalkar and A. Kendhe, “Detection of Brain Tumor from MRI Images by Using Segmentation & SVM,” World Conference on Futuristic Trends in Research and Innovation for Social Welfare, pp. 1-6, 2016.

A. Goel and V. P. Vishwakarma, “Feature Extraction Technique Using Hybridization of DWT and DCT for Gender Classification,” Ninth International Conference on Contemporary Computing (IC3), pp. 1-6, 2016.

D. Somwanshi, A. Kumar, P. Sharma and D. Joshi, “An Efficient Brain Tumor Detection from MRI Images using Entropy Measures,” International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-5, 2016.

S. Pereira, A. Oliveira, V. Alves and C. A. Silva, “On Hierarchical Brain Tumor Segmentation in MRI using Fully Convolutional Neural Networks: A preliminary study,” IEEE 5th Portuguese Meeting on Bioengineering (ENBENG), pp. 1-4, 2017.

V. Shreyas and V. Pankajakshan, “A deep learning architecture for brain tumor segmentation in MRI images,” IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6, 2017.

S. K. Shil, F. P. Polly, M. A. Hossain, M. S. Ifthekhar, M. N. Uddin and Y. M. Jang, “An Improved Brain Tumor Detection and Classification Mechanism,” International Conference on Information and Communication Technology Convergence (ICTC), pp. 54-57, 2017.

C. H. Rao, P. V. Naganjaneyulu and K. S. Prasad, “Brain Tumor Detection and Segmentation Using Conditional Random Field,” IEEE 7th International Advance Computing Conference (IACC), pp. 807-810, 2017.

T. M. Devi, G. Ramani and S. X. Arockiaraj, “MR Brain Tumor Classification and Segmentation via Wavelets,” International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1-4, 2018.

G. Raut, A. Raut, J. Bhagade, J. Bhagade and S. Gavhane, “Deep Learning Approach for Brain Tumor Detection and Segmentation,” International Conference on Convergence to Digital World - Quo Vadis (ICCDW), pp. 1-5, 2020.

A. S. Methil, “Brain Tumor Detection using Deep Learning and Image Processing,” International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 100-108, 2021.

M. Joseph, L. ., & Fredrik, E. J. T. . (2023). Protecting Information Stored Inside the Cloud with A New CCA-EBO Protocol Designed on Hive Technology. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 40–49. https://doi.org/10.17762/ijritcc.v11i4s.6305

Aoudni, Y., Donald, C., Farouk, A., Sahay, K. B., Babu, D. V., Tripathi, V., & Dhabliya, D. (2022). Cloud security based attack detection using transductive learning integrated with hidden markov model. Pattern Recognition Letters, 157, 16-26. doi:10.1016/j.patrec.2022.02.012

Dharmesh D, Natural Language Processing for Automated Document Summarization , Machine Learning Applications Conference Proceedings, Vol 3 2023.

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Published

03.09.2023

How to Cite

Wankhede, P. A. ., & Dixit, S. R. . (2023). Analysis of Diagnosis for Malignant and Benign Brain Tumor MRI Images using CNN and DWT Technique. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 27–37. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3392

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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