Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection


  • Pushpak Kurella Department of Electrical and Electronics Engineering, Conestoga College, Doon campus, Ontario, Canada


Brain tumor detection, Deep learning, Convolution neural networks, Hidden markov random field


The brain tissues segmented by MRI and CT provide a more accurate viewpoint on diagnosing various brain illnesses. Many different segmentation approaches may be used to brain MRI images. Some of the most successful include Histogram thresholding, area based segmentation (K-means, Expectation and Maximization (EM), Fuzzy connectivity, and Markov random fields (MRF). The Hidden Markov Random field (HMRF) approach is one of the most effective segmentation techniques available. It is capable of solving quickly distinct brain tissues for recognition purposes. Using the HMRF model allows for the reduction of energy consumption and the smoothing of images. In this work, the primary goal is to increase segmentation quality by implementing a unique Hidden Markov Random field model and employing MATLAB simulations to implement in Spatial Fuzzy, Iterative Conditional Mode (ICM) method, Fuzzy MRF technique, and Hidden Markov Random field model. The results will be compared to those obtained using Histogram thresholding, the Region Growing method (RGM), the k-means methodology, and the Expectation and Maximization methods to assess segmentation quality and noise reduction.


N. M. Dipu, S. A. Shohan and K. M. A. Salam, "Deep Learning Based Brain Tumor Detection and Classification," 2021 International Conference on Intelligent Technologies, Hubli, pp. 1-6, 2021.

S. K. Chandra and M. Kumar Bajpai, "Effective algorithm for benign brain tumor detection using fractional calculus," 2018 IEEE Region 10 Conference, Jeju, Korea (South), pp. 2408-2413, 2018.

L. C. Paul, M. N. Hossain, M. M. Mowla, M. Z. Mahmud, R. Azim and M. T. Islam, "Human Brain Tumor Detection Using CPW Fed UWB Vivaldi Antenna”, 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), Dhaka, Bangladesh, pp. 1-6, 2019.

T. P. Pries, R. Jahan and P. Suman, "Review of Brain Tumor Segmentation, Detection and Classification Algorithms in fMRI Images," 2018 International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES), Lucknow, pp. 300-303, 2018.

J. K. Periasamy, B. S and J. P, "Comparison of VGG-19 and RESNET-50 Algorithms in Brain Tumor Detection," 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, pp. 1-5, 2023.

S. Koshti, V. Degaonkar, I. Modi, I. Srivastava, J. Panambor and A. Jagtap, "Brain Tumor Detection System using Convolutional Neural Network," 2022 IEEE Pune Section International Conference (PuneCon), India, pp. 1-6, 2022.

M. M. Goswami and B. D. Rao, "Detection of Brain Tumor & Intra-Tumoral Structures from the Brain MR Images," 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India, pp. 1147-1152, 2022.

Y. Bhanothu, A. Kamalakannan and G. Rajamanickam, "Detection and Classification of Brain Tumor in MRI Images using Deep Convolutional Network," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp. 248-252, 2020.

R. Tamilselvi, A. Nagaraj, M. P. Beham and M. B. Sandhiya, "BRAMSIT: A Database for Brain Tumor Diagnosis and Detection," 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, pp. 1-5, 2020.

M. Kurnar, A. Sinha and N. V. Bansode, "Detection of Brain Tumor in MRI Images by Applying Segmentation and Area Calculation Method Using SCILAB," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, pp. 1-5, 2018.

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, pp. 430-434, 2021.

A. Jagan, "A New Approach for Segmentation and Detection of Brain Tumor in 3D Brain MR Imaging," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 1230-1235, 2018.

V. Kushwaha and P. Maidamwar, "An Empirical Analysis of Machine Learning Techniques for Brain Tumor Detection," 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, 2022, pp. 405-410, 2022.

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

M. C. S. Tang and S. S. Teoh, "Brain Tumor Detection from MRI Images Based on ResNet18," 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, pp. 1-5, 2023.

S. P. N, B. S, K. K. N and P. A. Vijaya, "Detection of Human Brain Tumors Using an UWB Patch Antenna at 28GHz," 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Bengaluru, India, pp. 96-100, 2023.

Additional Files



How to Cite

Kurella, P. (2023). Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection. International Transactions on Electrical Engineering and Computer Science, 2(4), 183–190. Retrieved from