Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python
DOI:
https://doi.org/10.4108/eetismla.5410Keywords:
Brain tumor, MRI, Detection, Analysis, Deep learning, CNN neural network, PythonAbstract
Medical image processing is a very difficult and new field. One thing they do in this field is analyze pictures of people's brains to look for signs of tumors. They use a special computer program to help with this. This paper talks about a new way to use the program to find brain cancer early by looking at the texture of the tumor. This paper explains how we can find and understand brain tumors using special pictures called MRI scans. We use computer programs to help us do this. First, we find the tumor, then we separate it from the rest of the brain, and finally we measure how big it is. We can also figure out how serious the tumor is by looking at different kinds of tumors. To make it easier for people to use, we made a special program in a computer language called COLAB for python codes about using CNN network for deep learning. We tested this program on 8 patients and learned a lot about their tumors.
Downloads
References
Hossain T, Shishir FS, Ashraf M, Al Nasim MA, Shah FM. Brain tumor detection using convolutional neural network. In2019 1st international conference on advances in science, engineering and robotics technology (ICASERT) 2019 May 3 (pp. 1-6). IEEE.
Sapra P, Singh R, Khurana S. Brain tumor detection using neural network. International Journal of Science and Modern Engineering (IJISME) ISSN. 2013 Aug:2319-6386.
Abdalla HE, Esmail MY. Brain tumor detection by using artificial neural network. In2018 international conference on computer, control, electrical, and electronics engineering (ICCCEEE) 2018 Aug 12 (pp. 1-6). IEEE.
Siar M, Teshnehlab M. Brain tumor detection using deep neural network and machine learning algorithm. In2019 9th international conference on computer and knowledge engineering (ICCKE) 2019 Oct 24 (pp. 363-368). IEEE.
Woźniak M, Siłka J, Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications. 2021 Mar 16:1-6.
Toğaçar M, Ergen B, Cömert Z. BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Medical hypotheses. 2020 Jan 1;134:109531.
Othman MF, Basri MA. Probabilistic neural network for brain tumor classification. In2011 Second International Conference on Intelligent Systems, Modelling and Simulation 2011 Jan 25 (pp. 136-138). IEEE.
Amin J, Sharif M, Yasmin M, Fernandes SL. Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems. 2018 Oct 1;87:290-7.
Dahab DA, Ghoniemy SS, Selim GM. Automated brain tumor detection and identification using image processing and probabilistic neural network techniques. International journal of image processing and visual communication. 2012 Oct;1(2):1-8.
Damodharan S, Raghavan D. Combining tissue segmentation and neural network for brain tumor detection. Int. Arab J. Inf. Technol.. 2015 Jan 1;12(1):42-52.
Hussein EM, Mahmoud DM. Brain tumor detection using artificial neural networks. Journal of Science and Technology. 2012 Dec;13(2):31-9.
Choudhury CL, Mahanty C, Kumar R, Mishra BK. Brain tumor detection and classification using convolutional neural network and deep neural network. In2020 international conference on computer science, engineering and applications (ICCSEA) 2020 Mar 13 (pp. 1-4). IEEE.
Samreen A, Taha A, Reddy Y, Sathish P. Brain Tumor Detection by Using Convolution Neural Network.
Shakeel PM, Tobely TE, Al-Feel H, Manogaran G, Baskar S. Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access. 2019 Jan 2;7:5577-88.
Febrianto DC, Soesanti I, Nugroho HA. Convolutional neural network for brain tumor detection. InIOP Conference Series: Materials Science and Engineering 2020 Mar 1 (Vol. 771, No. 1, p. 012031). IOP Publishing.
Irsheidat S, Duwairi R. Brain tumor detection using artificial convolutional neural networks. In2020 11th International Conference on Information and Communication Systems (ICICS) 2020 Apr 7 (pp. 197-203). IEEE.
Manjunath S, Pande MS, Raveesh BN, Madhusudhan GK. Brain tumor detection and classification using convolution neural network. Int. J. Recent Technol. Eng.(IJRTE). 2019;8(1):2277-3878.
Maqsood S, Damaševičius R, Maskeliūnas R. Multi-modal brain tumor detection using deep neural network and multiclass SVM. Medicina. 2022 Aug 12;58(8):1090.
Ural B. A computer-based brain tumor detection approach with advanced image processing and probabilistic neural network methods. Journal of Medical and Biological Engineering. 2018 Dec;38:867-79.
Li M, Kuang L, Xu S, Sha Z. Brain tumor detection based on multimodal information fusion and convolutional neural network. IEEE access. 2019 Dec 9;7:180134-46.
Millstein F. Convolutional neural networks in Python: beginner's guide to convolutional neural networks in Python. Frank Millstein; 2020 Jul 6.
Sewak M, Karim MR, Pujari P. Practical convolutional neural networks: implement advanced deep learning models using Python. Packt Publishing Ltd; 2018 Feb 27.
Malik J, Kiranyaz S, Gabbouj M. FastONN--Python based open-source GPU implementation for Operational Neural Networks. arXiv preprint arXiv:2006.02267. 2020 Jun 3.
Sharma A, Singh V, Rani A. Implementation of CNN on Zynq based FPGA for Real-time Object Detection. In2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2019 Jul 6 (pp. 1-7). IEEE.
Zafar I, Tzanidou G, Burton R, Patel N, Araujo L. Hands-on convolutional neural networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python. Packt Publishing Ltd; 2018 Aug 28.
Collobert R, Kavukcuoglu K, Farabet C. Implementing neural networks efficiently. InNeural Networks: Tricks of the Trade: Second Edition 2012 (pp. 537-557). Berlin, Heidelberg: Springer Berlin Heidelberg.
Sajanraj TD, Beena MV. Indian sign language numeral recognition using region of interest convolutional neural network. In2018 second international conference on inventive communication and computational technologies (ICICCT) 2018 Apr 20 (pp. 636-640). IEEE.
Pajankar A, Joshi A. Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch. Apress; 2022.
Sakib S, Ashrafi Z, Siddique MA. Implementation of fruits recognition classifier using convolutional neural network algorithm for observation of accuracies for various hidden layers. arXiv preprint arXiv:1904.00783. 2019 Apr 1.
Sarkar D, Bali R, Ghosh T. Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing Ltd; 2018 Aug 31.
Zaccone G, Karim MR. Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python. Packt Publishing Ltd; 2018 Mar 30.
Vasilev I. Advanced Deep Learning with Python: Design and implement advanced next- generation AI solutions using TensorFlow and PyTorch. Packt Publishing Ltd; 2019 Dec 12.
Kitamura G, Chung CY, Moore BE. Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation. Journal of digital imaging. 2019 Aug 15;32:672-7.
Loy J. Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects. Packt Publishing Ltd; 2019 Feb 28.
Shao Y, Hellström M, Mitev PD, Knijff L, Zhang C. PiNN: A python library for building atomic neural networks of molecules and materials. Journal of chemical information and modeling. 2020 Jan 14;60(3):1184-93.
Huynh TV. FPGA-based acceleration for convolutional neural networks on PYNQ-Z2. Int. J. Comput. Digit. Syst.. 2022 Jan;11(1).
Malathi M, Sinthia P. Brain tumour segmentation using convolutional neural network with tensor flow. Asian Pacific journal of cancer prevention: APJCP. 2019;20(7):2095.
Malathi M, Sinthia P. Brain tumour segmentation using convolutional neural network with tensor flow. Asian Pacific journal of cancer prevention: APJCP. 2019;20(7):2095.
Dua S, Kumar SS, Albagory Y, Ramalingam R, Dumka A, Singh R, Rashid M, Gehlot A, Alshamrani SS, AlGhamdi AS. Developing a Speech Recognition System for Recognizing Tonal Speech Signals Using a Convolutional Neural Network. Applied Sciences. 2022 Jun 19;12(12):6223.
Nguyen SN, Nguyen VQ, Choi J, Kim K. Design and implementation of intrusion detection system using convolutional neural network for DoS detection. InProceedings of the 2nd international conference on machine learning and soft computing 2018 Feb 2 (pp. 34-38).
Newsha Valadbeygi. (2023). Wet Cooling Tower Heat Transfer and Function Prediction using MLP Neural Network. https://doi.org/10.5281/zenodo.8420643
Newsha Valadbeygi. (2023). A Parametric Study to Predict Wind Energy Potential from Neural Network. https://doi.org/10.5281/zenodo.8420692
Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021 Aug 2;23(8):1231-1251. doi: 10.1093/neuonc/noab106. PMID: 34185076; PMCID: PMC8328013.
Seyed Masoud Ghoreishi Mokri, Newsha Valadbeygi, Irina G. Stelnikova (2024), Using Convolutional Neural Network to Design and Predict the Forces and Kinematic Performance and External Rotation Moment of the Hip Joint in the Pelvis. International Journal of Innovative Science and Research Technology (IJISRT) IJISRT24FEB1059, 878-883. DOI: 10.38124/ijisrt/IJISRT24FEB1059.
Seyed Masoud Ghoreishi Mokri; Newsha Valadbeygi; Khafaji Mohammed Balyasimovich. “Predicting the Performance and Adaptation of Artificial Elbow Due to Effective Forces using Deep Learning".” Volume. 9 Issue.3, March - 2024 International Journal of Innovative Science and Research Technology (IJISRT), www.ijisrt.com. ISSN - 2456-2165, PP :-651-657.:-https://doi.org/10.38124/ijisrt/IJISRT24MAR754.
Valadbeygi N, Shahrjerdi A. Prediction of Heating Energy Consumption in Houses via Deep Learning Neural Network. Analytical and Numerical Methods in Mechanical Design. 2022 Dec 1;1(2):11-6.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Seyed Masoud Ghoreishi Mokri , Newsha Valadbeygi, Vera Grigoryeva
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
