2D Encoding Convolution Neural Network Algorithm for Brain Tumour Prediction


  • F. Paulin Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore.
  • P. Lakshmi Bharathiar University, Coimbatore




Deep Learning, Magnetic Resonance Image (MRI), Convolutional Neural Network


In contemporary times, biomedical imaging plays a pivotal role in addressing various patient-related concerns.  Brain imaging, particularly through techniques like MRI, offers valuable insights crucial for surgical procedures, radiotherapy, treatment planning, and stereotactic neurosurgery. To facilitate the accurate identification of cancerous cells within the brain using MRI, deep learning and image classification techniques have been deployed. These technologies have paved the way for the development of automated tumor detection methods, which not only save valuable time for radiologists but also consistently deliver proven levels of accuracy. In contrast, the conventional approach to defect detection in magnetic resonance brain images relies on manual human inspection, a method rendered impractical due to the sheer volume of data This paper outlines an approach aimed at detecting and classifying brain tumors within patient MRI images. Additionally, it conducts a performance comparison of Convolutional Neural Network (CNN) models in this context.


P. Kumar and B. Vijayakumar (2015).”Brain tumour MR image segmentation and classification using by PCA and

RBF kernel-based support vector machine”,Middle-East Journal of Scientific Research, vol. 23, no. 9, pp. 2106–2116.

Nilesh Bhaskarrao Bahadure, Arun Kumar Ray and HarPal Thethi (2017).Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM Hindawi, International Journal of Biomedical Imaging Volume, Article ID 9749108, 12 pages.

Chaddad (2015).Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models, International Journal of Biomedical Imaging, vol. 2015, Article ID 868031, 11 pages.

S. N. Deepa and B. Arunadevi (2013). Extreme learning machine for classification of brain tumor in 3DMR images,

Informatologia, vol. 46, no. 2, pp. 111–121.

J. Sachdeva, V. Kumar, I. Gupta, N. Khandelwal, and C. K. Ahuja (2013). Segmentation, feature extraction, and

multiclass brain tumor classification, Journal of Digital Imaging, vol. 26, no. 6, pp. 1141–1150.

Pavel Dvorak, Walter Kropatsch, and Karel Bartusek (July-2015). Automatic Detection of Brain Tumors in MR

Images,” DOI: 10.1109/TSP.2013.6614000. Sonu Suhag, L. M. Saini Automatic Detection of Brain Tumor by Image

Processing in Matlab,” International Journal of Advances in Science Engineering and Technology, Volume- 3,


Latha, M., Surya, R (2016).Brain tumour detection using neural network classifier and k-means clustering algorithm for classification and segmentation. IIR J. Soft Comput. 1(1), 27–32. .

Raheleh Hashemzehi Seyyed Javad Seyyed Mahdavi Maryam Kheirabadi Seyed Reza Kamel (2020). Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences Online Public.

D. F. Specht. Probabilistic Neural Networks. Neural Networks, 3(1):109–118, 1990.

Buthayna G. Elshaikh , MEM Garelnabi, Hiba Omer, Abdelmoneim Sulieman, B. Habeeballa, Rania A. Tabeidi,

” Recognition of brain tumors in MRI images using texture analysis”, Saudi Journal of Biological Sciences 28 (2021)


Vasupradha Vijay, Dr.A.R.Kavitha,, S.Roselene Rebecca,” Automated Brain Tumor Segmentation and Detection

in MRI using Enhanced Darwinian Particle Swarm Optimization(EDPSO), Procedia Computer Science 92 (2016 ) 475 – 480.

Arkapravo Chattopadhyay, Mausumi Maitra,” MRIbased brain tumour image detection using CNN based

deep learning method” Neuroscience Informatics 2 (2022) 100060

Lamia H. Shehab, Omar M. Fahmy, Safa M. Gasser, Mohamed S. El-Mahallawy,” An efficient brain tumor image segmentation based on deep residual networks (ResNets) “, Journal of King Saud University – Engineering Sciences 33 (2021) 404–412.

C. Jaspin Jeba Sheela , G. Suganthi,” Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization “,Journal of King Saud University – Computer and Information Sciences 34 (2022)


Nivea Kesav, M.G. Jibukumar,” Efficient and low complex architecture for detection and classification of Brain Tumor

using RCNN with Two Channel CNN “, Journal of King Saud University – Computer and Information Sciences 34

(2022) 6229–6242.

Kamil Dimililer, Ahmet Ilhan , “Effect of image enhancement on MRI brain images with neural networks”, Procedia Computer Science 102 ( 2016 ) 39 – 44

Mansi Lathera,, Dr. Parvinder Singh, ” Investigating Brain Tumor Segmentation and Detection Techniques”, Procedia

Computer Science 167 (2020) 121–130

Remya Ajai A S, Sundararaman Gopalan, “Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm

for Brain Tumor MRI Images”, Procedia Computer Science 201 (2022) 487–494

Md Khairul Islam, Md Shahin Ali, Md Sipon Miah, Md Mahbubur Rahman, Md Shahariar Alam, Mohammad

Amzad Hossain, “Brain tumor detection in MR image using super pixels, principal component analysis and template based K-means clustering algorithm”, MachineLearning with Applications 5 (2021) 100044

Anand Deshpande, Vania V.Estrela, Prashant Patavardhan, “The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50”, Neuroscience Informatics 1 (2021) 100013

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